IMW 2020: Leaderboard

Current version: 6147c3cf (2021-05-11, 17:43 UTC)

Leaderboard: unlimited keypoints (8k), standard descriptors (512 bytes) (total: 144)
Leaderboard: restricted keypoints (2k), standard descriptors (512 bytes) (total: 94)
Leaderboard: unlimited keypoints (8k), small descriptors (128 bytes) (total: 2)
Leaderboard: restricted keypoints (2k), small descriptors (128 bytes) (total: 6)
Leaderboard: unlimited keypoints (8k), large descriptors (512 bytes) (total: 147)
Leaderboard: restricted keypoints (2k), large descriptors (512 bytes) (total: 108)

Categories are non-exclusive: e.g. small descriptors compete with larger descriptors, but not vice-versa. For the sake of clarity, we do separate 2k and 8k in the leaderboards. Please note that this is a static website: you may want to force a reload if it does not update properly. We are working on new features and tracks: the organizers reserve the right to update terms and conditions within the next few weeks.

Prize #1: unlimited keypoints (8k) / standard descriptors (512 bytes)

Stereo	Multiview	Combined
#1: [sid:00591] Guided-Hardnet-Epoch... mAA = 0.61081 #2: [sid:00624] Guided-HardNet-OANet mAA = 0.60261 #3: [sid:00590] Guided-HardNet-epoch... mAA = 0.59919	#1: [sid:00624] Guided-HardNet-OANet mAA = 0.78550 #2: [sid:00591] Guided-Hardnet-Epoch... mAA = 0.78288 #3: [sid:00610] Hardnet-Upright-AdaL... mAA = 0.77056	(Total: 144) #1: [sid:00591] Guided-Hardnet-Epoch... mAA = 0.69684 #2: [sid:00624] Guided-HardNet-OANet mAA = 0.69405 #3: [sid:00590] Guided-HardNet-epoch... mAA = 0.68069

Prize #2: restricted keypoints (2k) / standard descriptors (512 bytes)

Stereo	Multiview	Combined
#1: [sid:00612] SuperPoint-128d-adap... mAA = 0.59034 #2: [sid:00667] sp_ae_sg_degensac_xp mAA = 0.58918 #3: [sid:00603] SuperPoint-128d-mask... mAA = 0.56769	#1: [sid:00667] sp_ae_sg_degensac_xp mAA = 0.77685 #2: [sid:00612] SuperPoint-128d-adap... mAA = 0.77337 #3: [sid:00603] SuperPoint-128d-mask... mAA = 0.76987	(Total: 94) #1: [sid:00667] sp_ae_sg_degensac_xp mAA = 0.68301 #2: [sid:00612] SuperPoint-128d-adap... mAA = 0.68186 #3: [sid:00603] SuperPoint-128d-mask... mAA = 0.66878

Phototourism: unlimited keypoints / standard descriptors (512 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00081 LogPolar w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	591.21 Rank: 33/144	0.472 Rank: 102/144	0.832 Rank: 83/144	0.52385 (±0.00113) Rank: 57/144	415.77 Rank: 90/144	4054.60 Rank: 70/144	4.316 Rank: 84/144	0.432 Rank: 68/144	0.69284 (±0.00429) Rank: 70/144	0.60835 Rank: 63/144	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00121 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	398.65 Rank: 81/144	0.472 Rank: 88/144	0.863 Rank: 30/144	0.53852 (±0.00039) Rank: 44/144	356.56 Rank: 107/144	3550.63 Rank: 92/144	4.275 Rank: 89/144	0.439 Rank: 74/144	0.68875 (±0.00138) Rank: 76/144	0.61364 Rank: 52/144	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00142 CV-DoG-MKD-Concat-pyransac Size: 512 bytes. Matches: built-in	7860.77	208.02 Rank: 134/144	0.472 Rank: 82/144	0.854 Rank: 34/144	0.40612 (±0.00056) Rank: 118/144	348.03 Rank: 119/144	3507.39 Rank: 98/144	4.169 Rank: 112/144	0.471 Rank: 113/144	0.64763 (±0.00344) Rank: 108/144	0.52687 Rank: 117/144	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00562 HardNet64-train-all-raw64-balanc... Size: 512 bytes. Matches: built-in	7831.92	370.11 Rank: 91/144	0.486 Rank: 77/144	0.807 Rank: 114/144	0.45770 (±0.00060) Rank: 111/144	575.43 Rank: 44/144	4756.46 Rank: 33/144	4.563 Rank: 36/144	0.435 Rank: 72/144	0.69780 (±0.00191) Rank: 65/144	0.57775 Rank: 89/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-raw64-balance-l2-224000-raw64 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 224000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00602 retrained sosnet Size: 512 bytes. Matches: built-in	7861.11	340.97 Rank: 104/144	0.472 Rank: 102/144	0.840 Rank: 70/144	0.48990 (±0.00046) Rank: 88/144	623.55 Rank: 32/144	4812.36 Rank: 26/144	4.233 Rank: 101/144	0.458 Rank: 99/144	0.66502 (±0.00158) Rank: 95/144	0.57746 Rank: 90/144	sosnet (contact)	siftdef	sosnet (128 float32: 512 bytes)	default setting and retrained sosnet	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00576 Upright-Sift + X-Net-lib w/ DEGE... Size: 512 bytes. Matches: built-in	7830.16	465.31 Rank: 62/144	0.486 Rank: 44/144	0.844 Rank: 61/144	0.53156 (±0.00027) Rank: 50/144	720.29 Rank: 25/144	5398.84 Rank: 19/144	4.547 Rank: 41/144	0.408 Rank: 37/144	0.71966 (±0.00160) Rank: 41/144	0.62561 Rank: 46/144	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	sift	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00086 LogPolar-Upright w/ MAGSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	732.28 Rank: 15/144	0.486 Rank: 45/144	0.844 Rank: 63/144	0.54060 (±0.00047) Rank: 42/144	505.37 Rank: 68/144	4414.11 Rank: 52/144	4.518 Rank: 46/144	0.421 Rank: 52/144	0.71092 (±0.00251) Rank: 46/144	0.62576 Rank: 45/144	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00061 L2-Net-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	358.37 Rank: 96/144	0.486 Rank: 45/144	0.845 Rank: 56/144	0.52012 (±0.00106) Rank: 62/144	369.60 Rank: 105/144	3538.91 Rank: 95/144	4.407 Rank: 60/144	0.447 Rank: 89/144	0.68108 (±0.00177) Rank: 85/144	0.60060 Rank: 72/144	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00084 LogPolar-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	459.75 Rank: 63/144	0.486 Rank: 45/144	0.852 Rank: 42/144	0.52316 (±0.00034) Rank: 59/144	483.93 Rank: 70/144	4405.57 Rank: 55/144	4.542 Rank: 42/144	0.422 Rank: 53/144	0.70680 (±0.00247) Rank: 53/144	0.61498 Rank: 50/144	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00638 MKDNet-polar Size: 512 bytes. Matches: built-in	7862.74	337.43 Rank: 106/144	0.472 Rank: 91/144	0.847 Rank: 51/144	0.51376 (±0.00106) Rank: 70/144	552.91 Rank: 47/144	4786.70 Rank: 28/144	4.259 Rank: 96/144	0.434 Rank: 71/144	0.69907 (±0.00048) Rank: 63/144	0.60642 Rank: 69/144	(contact)	sift	mkdnet-polar (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-11	is_submission
Submission ID: 00140 CV-DoG-TFeat-kornia-MAGSAC Size: 512 bytes. Matches: built-in	7860.77	292.56 Rank: 118/144	0.472 Rank: 82/144	0.819 Rank: 106/144	0.46677 (±0.00109) Rank: 102/144	265.53 Rank: 137/144	2905.25 Rank: 125/144	4.038 Rank: 128/144	0.487 Rank: 120/144	0.62608 (±0.00138) Rank: 119/144	0.54643 Rank: 109/144	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	N/A	21-02-05	is_baseline
Submission ID: 00634 SIFT8k-HardNetPS-first-submit Size: 512 bytes. Matches: built-in	7830.09	502.15 Rank: 54/144	0.486 Rank: 31/144	0.841 Rank: 66/144	0.49113 (±0.00083) Rank: 85/144	651.33 Rank: 27/144	5100.71 Rank: 21/144	4.370 Rank: 73/144	0.423 Rank: 56/144	0.70782 (±0.00264) Rank: 51/144	0.59948 Rank: 73/144	Xudong Zhang, Yuhao Zhou, Huanhuan Fan (contact)	sift	hardnetps (128 float32: 512 bytes)	SIFT and hardnet with 8k features for fisrt subimission.	N/A	N/A	20-12-15	is_submission
Submission ID: 00601 SIFT8k-HardNet64 Size: 512 bytes. Matches: built-in	6589.88	381.80 Rank: 85/144	0.467 Rank: 123/144	0.852 Rank: 39/144	0.52493 (±0.00044) Rank: 55/144	420.36 Rank: 87/144	3586.82 Rank: 91/144	4.494 Rank: 51/144	0.429 Rank: 64/144	0.70580 (±0.00229) Rank: 55/144	0.61536 Rank: 49/144	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, hardnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-29	is_submission, is_challenge_2020
Submission ID: 00591 Guided-Hardnet-Epoch2 Size: 512 bytes. Matches: custom	7829.63	761.99 Rank: 13/144	0.486 Rank: 45/144	0.823 Rank: 102/144	0.61081 (±0.00000) Rank: 1/144	785.57 Rank: 23/144	6330.70 Rank: 7/144	4.680 Rank: 7/144	0.358 Rank: 2/144	0.78288 (±0.00094) Rank: 2/144	0.69684 Rank: 1/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-24	is_submission, is_challenge_2020
Submission ID: 00594 CV-DoG-HardNet8-PT Size: 512 bytes. Matches: built-in	7829.26	579.51 Rank: 37/144	0.484 Rank: 78/144	0.872 Rank: 15/144	0.58375 (±0.00046) Rank: 11/144	573.22 Rank: 45/144	4489.23 Rank: 47/144	4.619 Rank: 18/144	0.403 Rank: 31/144	0.72432 (±0.00357) Rank: 34/144	0.65403 Rank: 22/144	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift8k	h8e512pt (128 float32: 512 bytes)	HardNet8 with PCA compression	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00611 sift and hardnet64 train scale(1... Size: 512 bytes. Matches: built-in	7830.09	622.13 Rank: 22/144	0.486 Rank: 33/144	0.871 Rank: 17/144	0.58870 (±0.00041) Rank: 5/144	899.14 Rank: 14/144	6086.16 Rank: 12/144	4.647 Rank: 10/144	0.386 Rank: 15/144	0.75127 (±0.00234) Rank: 14/144	0.66999 Rank: 8/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-l2-val-14000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 14000), FLANN disabled	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00597 SIFT8k-HardNet64 Size: 512 bytes. Matches: built-in	6589.88	381.75 Rank: 86/144	0.467 Rank: 123/144	0.852 Rank: 40/144	0.52511 (±0.00056) Rank: 54/144	420.42 Rank: 86/144	3595.93 Rank: 90/144	4.496 Rank: 50/144	0.417 Rank: 45/144	0.70738 (±0.00218) Rank: 52/144	0.61625 Rank: 47/144	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, hardnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-27	is_submission, is_challenge_2020
Submission ID: 00636 MKDNet-cart Size: 512 bytes. Matches: built-in	7862.74	260.22 Rank: 126/144	0.472 Rank: 91/144	0.819 Rank: 107/144	0.46478 (±0.00044) Rank: 106/144	432.81 Rank: 83/144	4142.25 Rank: 68/144	4.050 Rank: 127/144	0.463 Rank: 104/144	0.66048 (±0.00254) Rank: 98/144	0.56263 Rank: 102/144	(contact)	sift	mkdnet-cart (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-13	is_submission
Submission ID: 00050 GeoDesc-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	410.23 Rank: 76/144	0.486 Rank: 45/144	0.827 Rank: 95/144	0.48259 (±0.00060) Rank: 94/144	394.67 Rank: 102/144	3863.42 Rank: 79/144	4.380 Rank: 67/144	0.443 Rank: 81/144	0.67682 (±0.00127) Rank: 90/144	0.57971 Rank: 87/144	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00076 SOSNet w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	563.27 Rank: 41/144	0.472 Rank: 102/144	0.846 Rank: 54/144	0.55170 (±0.00042) Rank: 32/144	508.66 Rank: 65/144	4502.19 Rank: 45/144	4.377 Rank: 71/144	0.405 Rank: 35/144	0.71820 (±0.00298) Rank: 42/144	0.63495 Rank: 35/144	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00511 r2d2-wasfi Size: 512 bytes. Matches: built-in	7861.03	276.55 Rank: 121/144	0.503 Rank: 17/144	0.762 Rank: 123/144	0.37893 (±0.00023) Rank: 125/144	416.35 Rank: 89/144	2834.69 Rank: 128/144	4.293 Rank: 88/144	0.480 Rank: 117/144	0.64359 (±0.00103) Rank: 111/144	0.51126 Rank: 124/144	Chen Shen (contact)	r2d2	r2d2-wasfi-epoch25-pretrained-n16-8k (128 float32: 512 bytes)	r2d2 with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00067 GeoDesc w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	453.39 Rank: 65/144	0.472 Rank: 102/144	0.835 Rank: 79/144	0.50559 (±0.00081) Rank: 77/144	395.12 Rank: 100/144	3838.97 Rank: 81/144	4.264 Rank: 94/144	0.442 Rank: 79/144	0.68032 (±0.00099) Rank: 88/144	0.59295 Rank: 82/144	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00079 LogPolar w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	441.77 Rank: 68/144	0.472 Rank: 102/144	0.852 Rank: 38/144	0.53396 (±0.00086) Rank: 48/144	415.77 Rank: 90/144	4054.60 Rank: 70/144	4.316 Rank: 84/144	0.432 Rank: 70/144	0.69284 (±0.00429) Rank: 70/144	0.61340 Rank: 53/144	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00508 modified-r2d2 Size: 512 bytes. Matches: built-in	7791.82	160.64 Rank: 141/144	0.517 Rank: 15/144	0.785 Rank: 119/144	0.32815 (±0.00037) Rank: 127/144	328.39 Rank: 128/144	2811.27 Rank: 129/144	4.250 Rank: 98/144	0.488 Rank: 121/144	0.62899 (±0.00193) Rank: 115/144	0.47857 Rank: 127/144	caoliang (contact)	modified-r2d2	modified-r2d2 (128 float32: 512 bytes)	r2d2 with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	https://europe.naverlabs.com/wp-content/uploads/2019/09/R2D2-Repeatable-and-Reliable-Detector-and-Descriptor-2.pdf	https://github.com/naver/r2d2	20-04-25	is_submission, is_challenge_2020
Submission ID: 00514 UprightRootSIFT-AdaLAM Size: 512 bytes. Matches: custom	6449.42	619.87 Rank: 23/144	0.436 Rank: 131/144	0.758 Rank: 125/144	0.31372 (±0.00000) Rank: 129/144	640.08 Rank: 30/144	5286.27 Rank: 20/144	4.343 Rank: 76/144	0.398 Rank: 25/144	0.72130 (±0.00085) Rank: 40/144	0.51751 Rank: 119/144	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Using upright RootSIFT with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance.	https://arxiv.org/abs/2006.04250	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00052 HardNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	363.51 Rank: 93/144	0.472 Rank: 102/144	0.853 Rank: 37/144	0.52325 (±0.00098) Rank: 58/144	402.15 Rank: 93/144	3895.00 Rank: 77/144	4.345 Rank: 74/144	0.426 Rank: 61/144	0.69653 (±0.00108) Rank: 66/144	0.60989 Rank: 60/144	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00501 Guided matching with Upright roo... Size: 512 bytes. Matches: custom	7829.27	609.35 Rank: 27/144	0.486 Rank: 29/144	0.786 Rank: 118/144	0.49072 (±0.00000) Rank: 86/144	1363.98 Rank: 3/144	7835.75 Rank: 1/144	4.219 Rank: 103/144	0.454 Rank: 95/144	0.68064 (±0.00390) Rank: 87/144	0.58568 Rank: 85/144	(contact)	sift	upright-root-sift (128 float32: 512 bytes)	In submission	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00122 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	528.67 Rank: 45/144	0.472 Rank: 88/144	0.838 Rank: 72/144	0.53291 (±0.00047) Rank: 49/144	356.56 Rank: 107/144	3550.63 Rank: 92/144	4.275 Rank: 89/144	0.443 Rank: 82/144	0.68875 (±0.00138) Rank: 76/144	0.61083 Rank: 58/144	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00558 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	333.94 Rank: 108/144	0.284 Rank: 140/144	0.427 Rank: 139/144	0.01806 (±0.00000) Rank: 142/144	341.75 Rank: 125/144	2003.56 Rank: 136/144	3.735 Rank: 135/144	0.593 Rank: 134/144	0.51048 (±0.00436) Rank: 134/144	0.26427 Rank: 138/144	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 10) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00614 ContextDesc Upright + Mutual Che... Size: 512 bytes. Matches: custom	7830.09	647.63 Rank: 18/144	0.487 Rank: 25/144	0.846 Rank: 55/144	0.57826 (±0.00000) Rank: 17/144	668.00 Rank: 26/144	5612.57 Rank: 16/144	4.666 Rank: 8/144	0.367 Rank: 4/144	0.77041 (±0.00298) Rank: 5/144	0.67433 Rank: 5/144	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00625 HardNet64-train-all-l2-val-14000... Size: 512 bytes. Matches: built-in	7830.09	605.12 Rank: 29/144	0.486 Rank: 33/144	0.872 Rank: 16/144	0.58776 (±0.00048) Rank: 7/144	899.14 Rank: 14/144	6095.78 Rank: 11/144	4.645 Rank: 11/144	0.389 Rank: 19/144	0.74849 (±0.00117) Rank: 17/144	0.66812 Rank: 11/144	Ximin Zheng, Sheng He, Guanlin Liang (contact)	sift8k	hardnet64-train-all-l2-val-14000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 14000), FLANN disabled	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00540 ASLV2+OANetV2+DEGENSAC Size: 512 bytes. Matches: custom	5982.87	1044.40 Rank: 5/144	0.550 Rank: 11/144	0.758 Rank: 124/144	0.50173 (±0.00000) Rank: 80/144	1067.11 Rank: 6/144	4587.47 Rank: 43/144	4.948 Rank: 2/144	0.386 Rank: 16/144	0.75275 (±0.00186) Rank: 13/144	0.62724 Rank: 43/144	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	aslv2	aslv2 (128 float32: 512 bytes)	ASL detector and descriptor, 8k keypoints, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00049 GeoDesc-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	317.71 Rank: 113/144	0.486 Rank: 45/144	0.850 Rank: 46/144	0.48769 (±0.00064) Rank: 90/144	394.67 Rank: 102/144	3863.42 Rank: 79/144	4.380 Rank: 67/144	0.440 Rank: 76/144	0.67682 (±0.00127) Rank: 90/144	0.58225 Rank: 86/144	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00600 ContextDesc Upright + Mutual Che... Size: 512 bytes. Matches: custom	7830.09	624.55 Rank: 20/144	0.487 Rank: 25/144	0.847 Rank: 53/144	0.57344 (±0.00000) Rank: 19/144	644.39 Rank: 29/144	5427.23 Rank: 18/144	4.700 Rank: 4/144	0.368 Rank: 5/144	0.77043 (±0.00133) Rank: 4/144	0.67194 Rank: 6/144	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-29	is_submission, is_challenge_2020
Submission ID: 00532 affnet-hardnet Size: 512 bytes. Matches: built-in	7925.53	226.75 Rank: 131/144	0.452 Rank: 129/144	0.649 Rank: 133/144	0.31958 (±0.00055) Rank: 128/144	243.28 Rank: 140/144	3155.72 Rank: 117/144	3.595 Rank: 137/144	0.554 Rank: 133/144	0.52578 (±0.00343) Rank: 133/144	0.42268 Rank: 130/144	caoliang (contact)	affnet	hardnet (128 float32: 512 bytes)	affnet up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1711.06704	https://github.com/ducha-aiki/affnet	20-04-24	is_submission, is_challenge_2020
Submission ID: 00071 L2-Net w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	366.00 Rank: 92/144	0.472 Rank: 102/144	0.852 Rank: 44/144	0.52953 (±0.00043) Rank: 51/144	339.00 Rank: 126/144	3424.94 Rank: 103/144	4.206 Rank: 105/144	0.452 Rank: 93/144	0.66445 (±0.00213) Rank: 96/144	0.59699 Rank: 77/144	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00074 L2-Net w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	481.03 Rank: 57/144	0.472 Rank: 102/144	0.830 Rank: 91/144	0.52519 (±0.00039) Rank: 53/144	339.00 Rank: 126/144	3424.94 Rank: 103/144	4.206 Rank: 105/144	0.448 Rank: 90/144	0.66445 (±0.00213) Rank: 96/144	0.59482 Rank: 81/144	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00054 HardNet w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	477.64 Rank: 58/144	0.472 Rank: 102/144	0.830 Rank: 90/144	0.51809 (±0.00053) Rank: 64/144	402.15 Rank: 93/144	3895.00 Rank: 77/144	4.345 Rank: 74/144	0.423 Rank: 58/144	0.69653 (±0.00108) Rank: 66/144	0.60731 Rank: 67/144	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00073 L2-Net-Upright w/ MAGSAC (no FLA... Size: 512 bytes. Matches: built-in	7829.63	577.91 Rank: 38/144	0.486 Rank: 45/144	0.837 Rank: 78/144	0.53911 (±0.00062) Rank: 43/144	395.53 Rank: 98/144	3603.85 Rank: 86/144	4.382 Rank: 64/144	0.455 Rank: 96/144	0.68491 (±0.00338) Rank: 81/144	0.61201 Rank: 57/144	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00572 Guided HardNet qht Size: 512 bytes. Matches: custom	7829.63	885.09 Rank: 8/144	0.486 Rank: 45/144	0.774 Rank: 122/144	0.54663 (±0.00000) Rank: 35/144	538.28 Rank: 52/144	4687.01 Rank: 35/144	4.644 Rank: 13/144	0.376 Rank: 10/144	0.75721 (±0.00140) Rank: 11/144	0.65192 Rank: 25/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-qht (128 float32: 512 bytes)	sift and hardnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00621 CV-DoG-HardNet8-PTv2 Size: 512 bytes. Matches: built-in	7830.09	583.12 Rank: 35/144	0.486 Rank: 31/144	0.880 Rank: 4/144	0.58997 (±0.00054) Rank: 4/144	577.37 Rank: 42/144	4476.61 Rank: 48/144	4.638 Rank: 15/144	0.407 Rank: 36/144	0.73096 (±0.00227) Rank: 24/144	0.66046 Rank: 17/144	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift8k	h8e512pt (128 float32: 512 bytes)	HardNet8 with PCA compression, batch sampling from few images	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00138 CV-DoG-TFeat-kornia-DEGENSAC Size: 512 bytes. Matches: built-in	7860.77	234.75 Rank: 130/144	0.472 Rank: 82/144	0.831 Rank: 86/144	0.46486 (±0.00039) Rank: 105/144	265.53 Rank: 137/144	2905.25 Rank: 125/144	4.038 Rank: 128/144	0.487 Rank: 119/144	0.62608 (±0.00138) Rank: 119/144	0.54547 Rank: 111/144	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	N/A	21-02-05	is_baseline
Submission ID: 00065 SOSNet-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	407.29 Rank: 78/144	0.486 Rank: 45/144	0.864 Rank: 29/144	0.54098 (±0.00086) Rank: 41/144	521.37 Rank: 60/144	4473.48 Rank: 49/144	4.597 Rank: 23/144	0.417 Rank: 43/144	0.72179 (±0.00330) Rank: 37/144	0.63139 Rank: 38/144	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00595 SIFT8k_8000_HardNet64-train-all-... Size: 512 bytes. Matches: built-in	7830.09	600.42 Rank: 31/144	0.486 Rank: 33/144	0.867 Rank: 23/144	0.58265 (±0.00102) Rank: 14/144	786.40 Rank: 22/144	5595.19 Rank: 17/144	4.603 Rank: 22/144	0.390 Rank: 20/144	0.74372 (±0.00317) Rank: 23/144	0.66319 Rank: 16/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-ben86-l2-210000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 210000 and iou 0.86), FLANN disabled	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00566 Guided-HardNet2qhtp.json Size: 512 bytes. Matches: custom	7829.63	517.72 Rank: 49/144	0.486 Rank: 45/144	0.874 Rank: 11/144	0.58251 (±0.00000) Rank: 15/144	448.12 Rank: 79/144	4072.82 Rank: 69/144	4.607 Rank: 20/144	0.387 Rank: 17/144	0.74640 (±0.00246) Rank: 20/144	0.66445 Rank: 14/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnetnd32qhtp (128 float32: 512 bytes)	sift and hardnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00080 LogPolar w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	376.42 Rank: 88/144	0.472 Rank: 102/144	0.837 Rank: 76/144	0.50188 (±0.00044) Rank: 79/144	399.70 Rank: 95/144	4036.37 Rank: 72/144	4.340 Rank: 77/144	0.429 Rank: 65/144	0.69100 (±0.00154) Rank: 73/144	0.59644 Rank: 78/144	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00019 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	7829.24	355.69 Rank: 97/144	0.487 Rank: 23/144	0.844 Rank: 64/144	0.51009 (±0.00033) Rank: 74/144	548.18 Rank: 49/144	4406.41 Rank: 54/144	4.385 Rank: 62/144	0.442 Rank: 78/144	0.68859 (±0.00254) Rank: 79/144	0.59934 Rank: 74/144	Challenge organizers (contact)	sift-lowth	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00062 L2-Net-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	471.61 Rank: 60/144	0.486 Rank: 45/144	0.822 Rank: 103/144	0.51450 (±0.00058) Rank: 68/144	369.60 Rank: 105/144	3538.91 Rank: 95/144	4.407 Rank: 60/144	0.448 Rank: 91/144	0.68108 (±0.00177) Rank: 85/144	0.59779 Rank: 76/144	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00628 Multiple kernel local descriptor Size: 512 bytes. Matches: built-in	7862.74	331.20 Rank: 109/144	0.472 Rank: 91/144	0.829 Rank: 92/144	0.48380 (±0.00048) Rank: 93/144	541.02 Rank: 51/144	4639.37 Rank: 40/144	4.144 Rank: 116/144	0.463 Rank: 105/144	0.66704 (±0.00645) Rank: 93/144	0.57542 Rank: 92/144	(contact)	sift	mkdpcawt (128 float32: 512 bytes)	based on the paper [Understanding and Improving Kernel Local Descriptors]	https://arxiv.org/pdf/1811.11147	https://arxiv.org/abs/1811.11147	20-10-28	is_submission
Submission ID: 00522 SIFT and HardNet64 train scale(5... Size: 512 bytes. Matches: built-in	7830.09	513.57 Rank: 51/144	0.486 Rank: 33/144	0.852 Rank: 45/144	0.55385 (±0.00076) Rank: 30/144	822.07 Rank: 18/144	5660.19 Rank: 15/144	4.555 Rank: 37/144	0.404 Rank: 33/144	0.72744 (±0.00455) Rank: 28/144	0.64065 Rank: 31/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-raw64 (128 float32: 512 bytes)	SIFT with 8000 keypoints(raw 64), hardnet64 with 128 descriptors, FLANN disabled	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00132 CV-DoG-AffNet-HardNet-kornia-MAG... Size: 512 bytes. Matches: built-in	7833.97	516.85 Rank: 50/144	0.486 Rank: 74/144	0.870 Rank: 19/144	0.54116 (±0.00028) Rank: 40/144	580.47 Rank: 38/144	4671.35 Rank: 37/144	4.565 Rank: 33/144	0.403 Rank: 27/144	0.72668 (±0.00115) Rank: 30/144	0.63392 Rank: 36/144	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00057 HardNet-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	582.52 Rank: 36/144	0.486 Rank: 45/144	0.841 Rank: 67/144	0.53730 (±0.00082) Rank: 46/144	480.13 Rank: 72/144	4167.34 Rank: 63/144	4.566 Rank: 31/144	0.416 Rank: 42/144	0.71542 (±0.00274) Rank: 44/144	0.62636 Rank: 44/144	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00048 GeoDesc w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	362.25 Rank: 94/144	0.472 Rank: 102/144	0.816 Rank: 110/144	0.46887 (±0.00025) Rank: 99/144	350.43 Rank: 117/144	3601.67 Rank: 88/144	4.234 Rank: 99/144	0.447 Rank: 88/144	0.65553 (±0.00444) Rank: 102/144	0.56220 Rank: 103/144	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00120 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	265.75 Rank: 124/144	0.472 Rank: 88/144	0.873 Rank: 13/144	0.46073 (±0.00062) Rank: 110/144	356.56 Rank: 107/144	3550.63 Rank: 92/144	4.275 Rank: 89/144	0.439 Rank: 75/144	0.68875 (±0.00138) Rank: 76/144	0.57474 Rank: 93/144	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00502 Guided matching with Upright roo... Size: 512 bytes. Matches: custom	7829.27	491.96 Rank: 56/144	0.486 Rank: 29/144	0.828 Rank: 94/144	0.50310 (±0.01761) Rank: 78/144	809.76 Rank: 20/144	5966.91 Rank: 13/144	4.462 Rank: 54/144	0.425 Rank: 60/144	0.71009 (±0.00414) Rank: 48/144	0.60659 Rank: 68/144	(contact)	sift	upright-root-sift (128 float32: 512 bytes)	In submission	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00510 sift and hardnet64 Size: 512 bytes. Matches: built-in	7861.62	326.87 Rank: 110/144	0.472 Rank: 100/144	0.845 Rank: 59/144	0.51149 (±0.00018) Rank: 72/144	521.58 Rank: 59/144	4587.88 Rank: 42/144	4.222 Rank: 102/144	0.424 Rank: 59/144	0.69458 (±0.00340) Rank: 68/144	0.60304 Rank: 70/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-v2 (128 float32: 512 bytes)	SIFT with 8000 keypoints, hardnet64 with 128 descriptors	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00106 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	326.20 Rank: 111/144	0.490 Rank: 20/144	0.809 Rank: 113/144	0.46326 (±0.00089) Rank: 107/144	324.62 Rank: 129/144	3030.67 Rank: 121/144	4.173 Rank: 109/144	0.462 Rank: 103/144	0.62829 (±0.00112) Rank: 116/144	0.54578 Rank: 110/144	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00618 Sift-Sem + HyNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7530.17	476.84 Rank: 59/144	0.490 Rank: 19/144	0.882 Rank: 3/144	0.57281 (±0.00047) Rank: 21/144	809.82 Rank: 19/144	5868.33 Rank: 14/144	4.585 Rank: 26/144	0.390 Rank: 22/144	0.74557 (±0.00393) Rank: 21/144	0.65919 Rank: 19/144	Barroso-Laguna, Axel and Tian, Yurun, Ng, Tony and Mikolajczyk, Krystian (contact)	sift-semantics	hynet (128 float32: 512 bytes)		N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00068 GeoDesc-Upright w/ DEGENSAC (no ... Size: 512 bytes. Matches: built-in	7829.63	409.87 Rank: 77/144	0.486 Rank: 45/144	0.869 Rank: 20/144	0.52674 (±0.00049) Rank: 52/144	458.60 Rank: 75/144	4146.83 Rank: 66/144	4.412 Rank: 58/144	0.431 Rank: 67/144	0.70442 (±0.00189) Rank: 57/144	0.61558 Rank: 48/144	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00070 GeoDesc-Upright w/ MAGSAC (no FL... Size: 512 bytes. Matches: built-in	7829.63	534.05 Rank: 44/144	0.486 Rank: 45/144	0.848 Rank: 49/144	0.52078 (±0.00028) Rank: 61/144	458.60 Rank: 75/144	4146.83 Rank: 66/144	4.412 Rank: 58/144	0.432 Rank: 69/144	0.70442 (±0.00189) Rank: 57/144	0.61260 Rank: 56/144	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00537 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	1476.71 Rank: 1/144	0.284 Rank: 140/144	0.145 Rank: 144/144	0.00178 (±0.00000) Rank: 144/144	1506.82 Rank: 2/144	2538.44 Rank: 132/144	3.388 Rank: 141/144	0.669 Rank: 139/144	0.42682 (±0.00259) Rank: 139/144	0.21430 Rank: 141/144	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished]	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00536 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	1164.84 Rank: 3/144	0.368 Rank: 135/144	0.341 Rank: 141/144	0.00607 (±0.00000) Rank: 143/144	1191.31 Rank: 4/144	2215.65 Rank: 134/144	4.014 Rank: 132/144	0.524 Rank: 132/144	0.58741 (±0.00250) Rank: 128/144	0.29674 Rank: 136/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished]	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00075 SOSNet w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	424.61 Rank: 74/144	0.472 Rank: 102/144	0.868 Rank: 22/144	0.55867 (±0.00060) Rank: 26/144	508.66 Rank: 65/144	4502.19 Rank: 45/144	4.377 Rank: 71/144	0.404 Rank: 32/144	0.71820 (±0.00298) Rank: 42/144	0.63844 Rank: 32/144	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-23	is_baseline
Submission ID: 00059 L2-Net w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	297.34 Rank: 117/144	0.472 Rank: 102/144	0.835 Rank: 81/144	0.49523 (±0.00042) Rank: 83/144	314.54 Rank: 132/144	3314.19 Rank: 112/144	4.204 Rank: 107/144	0.467 Rank: 110/144	0.65665 (±0.00287) Rank: 99/144	0.57594 Rank: 91/144	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00042 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	7150.21	161.98 Rank: 139/144	0.514 Rank: 16/144	0.653 Rank: 131/144	0.16159 (±0.00090) Rank: 134/144	910.31 Rank: 13/144	1423.38 Rank: 142/144	2.722 Rank: 143/144	0.897 Rank: 144/144	0.08054 (±0.00242) Rank: 144/144	0.12106 Rank: 144/144	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00520 SIFT and HardNet64 train scale(5... Size: 512 bytes. Matches: built-in	7830.09	400.19 Rank: 80/144	0.486 Rank: 33/144	0.837 Rank: 77/144	0.50589 (±0.00059) Rank: 76/144	619.25 Rank: 33/144	4959.78 Rank: 24/144	4.384 Rank: 63/144	0.456 Rank: 97/144	0.69172 (±0.00048) Rank: 72/144	0.59880 Rank: 75/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-scale5 (128 float32: 512 bytes)	SIFT with 8000 keypoints(size scaled by 5), hardnet64 with 128 descriptors, FLANN disabled	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00055 HardNet-Upright w/ DEGENSAC (no ... Size: 512 bytes. Matches: built-in	7829.63	527.59 Rank: 47/144	0.486 Rank: 45/144	0.876 Rank: 7/144	0.57279 (±0.00120) Rank: 22/144	509.07 Rank: 63/144	4250.40 Rank: 58/144	4.548 Rank: 39/144	0.413 Rank: 40/144	0.72309 (±0.00141) Rank: 35/144	0.64794 Rank: 27/144	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00613 HardNet64-data-aug-sort-51 Size: 512 bytes. Matches: built-in	7830.09	624.09 Rank: 21/144	0.486 Rank: 33/144	0.870 Rank: 18/144	0.58727 (±0.00076) Rank: 8/144	964.80 Rank: 9/144	6350.68 Rank: 5/144	4.644 Rank: 14/144	0.383 Rank: 13/144	0.74952 (±0.00162) Rank: 16/144	0.66839 Rank: 10/144	Ximin Zheng, Sheng He, Guanlin Liang (contact)	sift8k	hardnet64-data-aug-sort-51 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 124000 and data augument), FLANN disabled	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00554 ContextDesc-Upright w/ DEGENSAC ... Size: 512 bytes. Matches: built-in	7830.09	470.69 Rank: 61/144	0.487 Rank: 25/144	0.877 Rank: 5/144	0.55697 (±0.00079) Rank: 28/144	543.45 Rank: 50/144	4543.76 Rank: 44/144	4.498 Rank: 49/144	0.405 Rank: 34/144	0.72902 (±0.00255) Rank: 27/144	0.64299 Rank: 30/144	Zixin Luo, Jiahui Zhang, Hongkai Chen (contact)	sift-def	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value and use only single orientation to increase performance. FLANN disabled.	https://arxiv.org/abs/1904.04084	https://github.com/lzx551402/contextdesc	20-05-11	is_submission, is_challenge_2020
Submission ID: 00571 ContextDesc Upright + OANetV2 + ... Size: 512 bytes. Matches: custom	7830.09	827.45 Rank: 9/144	0.487 Rank: 25/144	0.775 Rank: 120/144	0.53810 (±0.00000) Rank: 45/144	855.38 Rank: 16/144	6561.52 Rank: 3/144	4.522 Rank: 44/144	0.370 Rank: 8/144	0.76702 (±0.00081) Rank: 6/144	0.65256 Rank: 23/144	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-21	is_submission, is_challenge_2020
Submission ID: 00078 SOSNet-Upright w/ MAGSAC (no FLA... Size: 512 bytes. Matches: built-in	7829.63	679.94 Rank: 17/144	0.486 Rank: 45/144	0.855 Rank: 33/144	0.56626 (±0.00020) Rank: 23/144	600.23 Rank: 35/144	4765.43 Rank: 31/144	4.584 Rank: 27/144	0.403 Rank: 28/144	0.73027 (±0.00284) Rank: 25/144	0.64826 Rank: 26/144	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00630 Superpoint_modified Size: 512 bytes. Matches: built-in	7927.15	25.26 Rank: 144/144	0.410 Rank: 134/144	0.328 Rank: 142/144	0.06569 (±0.00036) Rank: 137/144	70.01 Rank: 144/144	895.58 Rank: 144/144	2.963 Rank: 142/144	0.849 Rank: 143/144	0.18293 (±0.00230) Rank: 142/144	0.12431 Rank: 143/144	Anonymous (to be released: 2020-6-12)	superpoint-modified	superpoint-modified (128 float32: 512 bytes)	Modified superpoint output.	N/A	N/A	20-11-27	is_submission
Submission ID: 00063 SOSNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	340.09 Rank: 105/144	0.472 Rank: 102/144	0.854 Rank: 35/144	0.51891 (±0.00067) Rank: 63/144	440.21 Rank: 80/144	4178.82 Rank: 61/144	4.378 Rank: 69/144	0.419 Rank: 48/144	0.70078 (±0.00375) Rank: 60/144	0.60984 Rank: 61/144	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00548 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	343.78 Rank: 102/144	0.368 Rank: 135/144	0.607 Rank: 135/144	0.28795 (±0.00000) Rank: 131/144	351.53 Rank: 113/144	1699.08 Rank: 139/144	4.124 Rank: 120/144	0.507 Rank: 126/144	0.61370 (±0.00246) Rank: 122/144	0.45082 Rank: 129/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00521 SIFT8k-gift Size: 512 bytes. Matches: built-in	6589.48	426.77 Rank: 73/144	0.467 Rank: 126/144	0.830 Rank: 87/144	0.48128 (±0.00056) Rank: 95/144	452.18 Rank: 77/144	3770.17 Rank: 85/144	4.595 Rank: 25/144	0.444 Rank: 84/144	0.67443 (±0.00253) Rank: 92/144	0.57785 Rank: 88/144	Chen Shen (contact)	sift-def	gift (128 float32: 512 bytes)	sift-nodups, gift with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00504 ASLFeat (MS) w/ DEGENSAC (no FLA... Size: 512 bytes. Matches: built-in	6948.83	390.79 Rank: 82/144	0.550 Rank: 10/144	0.797 Rank: 116/144	0.46102 (±0.00067) Rank: 109/144	510.80 Rank: 62/144	3817.14 Rank: 83/144	4.577 Rank: 29/144	0.444 Rank: 83/144	0.68246 (±0.00173) Rank: 84/144	0.57174 Rank: 97/144	Zixin Luo, Jiahui Zhang (contact)	aslfeat	aslfeat (128 float32: 512 bytes)	ASLFeat (MS) with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC. FLANN disabled.	https://arxiv.org/abs/2003.10071	https://github.com/lzx551402/ASLFeat	20-04-25	is_submission, is_challenge_2020
Submission ID: 00010 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	7857.11	246.74 Rank: 128/144	0.553 Rank: 9/144	0.735 Rank: 127/144	0.30717 (±0.00122) Rank: 130/144	479.55 Rank: 74/144	2778.68 Rank: 130/144	3.393 Rank: 140/144	0.737 Rank: 142/144	0.36048 (±0.00382) Rank: 141/144	0.33383 Rank: 132/144	Challenge organizers (contact)	akaze-lowth	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00141 CV-DoG-MKD-Concat-DEGENSAC Size: 512 bytes. Matches: built-in	7860.77	305.75 Rank: 115/144	0.472 Rank: 82/144	0.845 Rank: 58/144	0.48465 (±0.00122) Rank: 92/144	348.03 Rank: 119/144	3507.39 Rank: 98/144	4.169 Rank: 112/144	0.467 Rank: 109/144	0.64763 (±0.00344) Rank: 108/144	0.56614 Rank: 99/144	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00051 HardNet w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	432.32 Rank: 72/144	0.472 Rank: 102/144	0.866 Rank: 25/144	0.55430 (±0.00031) Rank: 29/144	426.84 Rank: 84/144	4001.40 Rank: 75/144	4.339 Rank: 79/144	0.419 Rank: 47/144	0.70962 (±0.00191) Rank: 49/144	0.63196 Rank: 37/144	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00639 MKDNet-hardnet Size: 512 bytes. Matches: built-in	7862.74	341.96 Rank: 103/144	0.472 Rank: 91/144	0.847 Rank: 52/144	0.51643 (±0.00099) Rank: 65/144	558.95 Rank: 46/144	4811.45 Rank: 27/144	4.268 Rank: 92/144	0.420 Rank: 51/144	0.69845 (±0.00564) Rank: 64/144	0.60744 Rank: 65/144	(contact)	sift	mkdnet-hardnet (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-11	is_submission
Submission ID: 00005 SURF (OpenCV), DEGENSAC Size: 256 bytes. Matches: built-in	7728.57	125.94 Rank: 142/144	0.432 Rank: 133/144	0.684 Rank: 130/144	0.24948 (±0.00065) Rank: 132/144	928.29 Rank: 12/144	3455.28 Rank: 102/144	3.428 Rank: 138/144	0.733 Rank: 141/144	0.40490 (±0.00448) Rank: 140/144	0.32719 Rank: 133/144	Challenge organizers (contact)	surf-lowth	surf (64 float32: 256 bytes)	SURF with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00069 GeoDesc w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	348.52 Rank: 101/144	0.472 Rank: 102/144	0.856 Rank: 32/144	0.51112 (±0.00070) Rank: 73/144	395.12 Rank: 100/144	3838.97 Rank: 81/144	4.264 Rank: 94/144	0.443 Rank: 80/144	0.68032 (±0.00099) Rank: 88/144	0.59572 Rank: 80/144	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00559 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	164.43 Rank: 138/144	0.284 Rank: 140/144	0.650 Rank: 132/144	0.05206 (±0.00000) Rank: 139/144	167.59 Rank: 142/144	1549.91 Rank: 140/144	3.989 Rank: 134/144	0.622 Rank: 136/144	0.47700 (±0.00167) Rank: 136/144	0.26453 Rank: 137/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 5) [Mishkin 2019]	N/A	N/A	20-05-10	is_submission, is_challenge_2020
Submission ID: 00006 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7829.24	319.29 Rank: 112/144	0.487 Rank: 23/144	0.830 Rank: 89/144	0.48742 (±0.00042) Rank: 91/144	525.57 Rank: 56/144	4147.20 Rank: 65/144	4.258 Rank: 97/144	0.460 Rank: 100/144	0.65664 (±0.00231) Rank: 101/144	0.57203 Rank: 96/144	Challenge organizers (contact)	sift-lowth	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00573 Guided-HardNet-qht Size: 512 bytes. Matches: custom	7829.63	887.83 Rank: 7/144	0.486 Rank: 45/144	0.774 Rank: 121/144	0.55224 (±0.00000) Rank: 31/144	538.28 Rank: 52/144	4693.85 Rank: 34/144	4.650 Rank: 9/144	0.381 Rank: 11/144	0.75930 (±0.00177) Rank: 8/144	0.65577 Rank: 20/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-qht (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00066 SOSNet-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	537.85 Rank: 43/144	0.486 Rank: 45/144	0.841 Rank: 68/144	0.53531 (±0.00061) Rank: 47/144	521.37 Rank: 60/144	4473.48 Rank: 49/144	4.597 Rank: 23/144	0.414 Rank: 41/144	0.72179 (±0.00330) Rank: 37/144	0.62855 Rank: 42/144	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00114 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	348.95 Rank: 100/144	0.547 Rank: 12/144	0.800 Rank: 115/144	0.43347 (±0.00084) Rank: 115/144	347.39 Rank: 122/144	3209.10 Rank: 114/144	4.126 Rank: 117/144	0.517 Rank: 128/144	0.58657 (±0.00120) Rank: 129/144	0.51002 Rank: 125/144	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00624 Guided-HardNet-OANet Size: 512 bytes. Matches: custom	7829.63	765.34 Rank: 12/144	0.486 Rank: 45/144	0.820 Rank: 104/144	0.60261 (±0.00000) Rank: 2/144	788.49 Rank: 21/144	6346.62 Rank: 6/144	4.682 Rank: 6/144	0.355 Rank: 1/144	0.78550 (±0.00157) Rank: 1/144	0.69405 Rank: 2/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 8k features, first using the oanet trained from scratch then guided-matching, setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00015 Root-SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7860.73	274.85 Rank: 122/144	0.472 Rank: 97/144	0.845 Rank: 57/144	0.48887 (±0.00011) Rank: 89/144	437.74 Rank: 82/144	3814.80 Rank: 84/144	4.151 Rank: 115/144	0.458 Rank: 98/144	0.65067 (±0.00155) Rank: 104/144	0.56977 Rank: 98/144	Challenge organizers (contact)	sift-lowth	rootsift (128 float32: 512 bytes)	Root-SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00102 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	815.40 Rank: 10/144	0.582 Rank: 2/144	0.788 Rank: 117/144	0.47395 (±0.00073) Rank: 97/144	356.21 Rank: 110/144	3366.01 Rank: 106/144	4.319 Rank: 81/144	0.464 Rank: 106/144	0.64829 (±0.00174) Rank: 105/144	0.56112 Rank: 104/144	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00519 SOSNet-Upright-AdaLAM Size: 512 bytes. Matches: custom	7830.09	803.50 Rank: 11/144	0.486 Rank: 33/144	0.818 Rank: 108/144	0.56056 (±0.00000) Rank: 25/144	827.85 Rank: 17/144	6157.62 Rank: 10/144	4.700 Rank: 5/144	0.381 Rank: 12/144	0.75917 (±0.00446) Rank: 9/144	0.65987 Rank: 18/144	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	Using upright SOSNet descriptors with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00024 SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7860.73	238.66 Rank: 129/144	0.472 Rank: 97/144	0.824 Rank: 99/144	0.45426 (±0.00097) Rank: 112/144	418.86 Rank: 88/144	3515.63 Rank: 97/144	4.001 Rank: 133/144	0.502 Rank: 125/144	0.60193 (±0.00185) Rank: 126/144	0.52810 Rank: 116/144	Challenge organizers (contact)	sift-lowth	sift (128 float32: 512 bytes)	SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00056 HardNet-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	439.36 Rank: 69/144	0.486 Rank: 45/144	0.864 Rank: 28/144	0.54393 (±0.00027) Rank: 39/144	480.13 Rank: 72/144	4167.34 Rank: 63/144	4.566 Rank: 31/144	0.417 Rank: 44/144	0.71542 (±0.00274) Rank: 44/144	0.62967 Rank: 41/144	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00047 GeoDesc w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	281.36 Rank: 120/144	0.472 Rank: 102/144	0.838 Rank: 75/144	0.47259 (±0.00033) Rank: 98/144	350.43 Rank: 117/144	3601.67 Rank: 88/144	4.234 Rank: 99/144	0.447 Rank: 87/144	0.65553 (±0.00444) Rank: 102/144	0.56406 Rank: 101/144	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00072 L2-Net-Upright w/ DEGENSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	435.68 Rank: 70/144	0.486 Rank: 45/144	0.859 Rank: 31/144	0.54497 (±0.00003) Rank: 36/144	395.53 Rank: 98/144	3603.85 Rank: 86/144	4.382 Rank: 64/144	0.452 Rank: 94/144	0.68491 (±0.00338) Rank: 81/144	0.61494 Rank: 51/144	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00518 UprightRootSIFT-AdaLAM Size: 512 bytes. Matches: custom	6449.42	435.53 Rank: 71/144	0.436 Rank: 131/144	0.825 Rank: 98/144	0.49625 (±0.00000) Rank: 82/144	449.49 Rank: 78/144	4353.56 Rank: 57/144	4.453 Rank: 56/144	0.397 Rank: 24/144	0.72490 (±0.00190) Rank: 33/144	0.61057 Rank: 59/144	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Using upright RootSIFT with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00526 ASLFeat-MS Size: 512 bytes. Matches: built-in	7384.19	456.55 Rank: 64/144	0.578 Rank: 5/144	0.752 Rank: 126/144	0.42208 (±0.00054) Rank: 116/144	532.83 Rank: 55/144	4002.80 Rank: 74/144	4.521 Rank: 45/144	0.447 Rank: 86/144	0.66568 (±0.00347) Rank: 94/144	0.54388 Rank: 113/144	Zixin Luo, Jiahui Zhang (contact)	aslfeat-ms	aslfeat-ms (128 float32: 512 bytes)	ASLFeat (joint predition of detectors and descriptors with multi-scale input) with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: 00627 Multiple kernel local descriptor Size: 512 bytes. Matches: built-in	7862.74	354.57 Rank: 98/144	0.472 Rank: 91/144	0.845 Rank: 60/144	0.50919 (±0.00037) Rank: 75/144	580.16 Rank: 41/144	4784.46 Rank: 29/144	4.210 Rank: 104/144	0.436 Rank: 73/144	0.68323 (±0.00354) Rank: 83/144	0.59621 Rank: 79/144	(contact)	sift	mkdlw (128 float32: 512 bytes)	based on the paper [Understanding and Improving Kernel Local Descriptors]	https://arxiv.org/pdf/1811.11147	https://arxiv.org/abs/1811.11147	20-10-28	is_submission
Submission ID: 00143 CV-DoG-MKD-Concat-magsac Size: 512 bytes. Matches: built-in	7860.77	381.37 Rank: 87/144	0.472 Rank: 82/144	0.833 Rank: 82/144	0.48096 (±0.00051) Rank: 96/144	348.03 Rank: 119/144	3507.39 Rank: 98/144	4.169 Rank: 112/144	0.471 Rank: 112/144	0.64763 (±0.00344) Rank: 108/144	0.56429 Rank: 100/144	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00083 LogPolar-Upright w/ DEGENSAC (no... Size: 512 bytes. Matches: built-in	7829.63	543.18 Rank: 42/144	0.486 Rank: 45/144	0.865 Rank: 27/144	0.55102 (±0.00015) Rank: 33/144	505.37 Rank: 68/144	4414.11 Rank: 52/144	4.518 Rank: 46/144	0.422 Rank: 54/144	0.71092 (±0.00251) Rank: 46/144	0.63097 Rank: 39/144	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00053 HardNet w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	575.07 Rank: 39/144	0.472 Rank: 102/144	0.842 Rank: 65/144	0.55022 (±0.00001) Rank: 34/144	426.84 Rank: 84/144	4001.40 Rank: 75/144	4.339 Rank: 79/144	0.419 Rank: 50/144	0.70962 (±0.00191) Rank: 49/144	0.62992 Rank: 40/144	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00557 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	614.16 Rank: 24/144	0.368 Rank: 135/144	0.513 Rank: 137/144	0.05482 (±0.00000) Rank: 138/144	626.57 Rank: 31/144	1793.00 Rank: 138/144	4.314 Rank: 86/144	0.517 Rank: 129/144	0.56933 (±0.00268) Rank: 132/144	0.31207 Rank: 134/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 10) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00113 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	290.24 Rank: 119/144	0.547 Rank: 12/144	0.814 Rank: 111/144	0.44501 (±0.00077) Rank: 114/144	347.39 Rank: 122/144	3209.10 Rank: 114/144	4.126 Rank: 117/144	0.519 Rank: 131/144	0.58657 (±0.00120) Rank: 129/144	0.51579 Rank: 120/144	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00130 CV-DoG-AffNet-HardNet-kornia-DEG... Size: 512 bytes. Matches: built-in	7833.97	403.41 Rank: 79/144	0.486 Rank: 74/144	0.883 Rank: 2/144	0.54468 (±0.00090) Rank: 37/144	580.47 Rank: 38/144	4671.35 Rank: 37/144	4.565 Rank: 33/144	0.402 Rank: 26/144	0.72668 (±0.00115) Rank: 30/144	0.63568 Rank: 34/144	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00533 upright-sift8k-hardnet Size: 512 bytes. Matches: built-in	6589.88	371.42 Rank: 90/144	0.467 Rank: 123/144	0.852 Rank: 43/144	0.52463 (±0.00070) Rank: 56/144	404.02 Rank: 92/144	3501.62 Rank: 101/144	4.483 Rank: 52/144	0.428 Rank: 63/144	0.70123 (±0.00081) Rank: 59/144	0.61293 Rank: 54/144	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-24	is_submission, is_challenge_2020
Submission ID: 00541 sift and sosnet64 train scale(12... Size: 512 bytes. Matches: built-in	7830.09	603.56 Rank: 30/144	0.486 Rank: 33/144	0.866 Rank: 24/144	0.58268 (±0.00061) Rank: 13/144	957.95 Rank: 10/144	6312.38 Rank: 8/144	4.607 Rank: 21/144	0.385 Rank: 14/144	0.74476 (±0.00137) Rank: 22/144	0.66372 Rank: 15/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-sos (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 348000), FLANN disabled	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00563 HardNet64-train-all-SOS-812000 Size: 512 bytes. Matches: built-in	7830.09	612.51 Rank: 25/144	0.486 Rank: 33/144	0.869 Rank: 21/144	0.58664 (±0.00095) Rank: 9/144	972.47 Rank: 8/144	6369.03 Rank: 4/144	4.612 Rank: 19/144	0.388 Rank: 18/144	0.74668 (±0.00251) Rank: 18/144	0.66666 Rank: 13/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00105 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	261.60 Rank: 125/144	0.490 Rank: 20/144	0.826 Rank: 97/144	0.46555 (±0.00055) Rank: 104/144	324.62 Rank: 129/144	3030.67 Rank: 121/144	4.173 Rank: 109/144	0.461 Rank: 101/144	0.62829 (±0.00112) Rank: 116/144	0.54692 Rank: 108/144	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00110 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	317.13 Rank: 114/144	0.482 Rank: 79/144	0.820 Rank: 105/144	0.46657 (±0.00079) Rank: 103/144	311.54 Rank: 134/144	3061.54 Rank: 118/144	4.105 Rank: 121/144	0.475 Rank: 115/144	0.62964 (±0.00428) Rank: 112/144	0.54810 Rank: 107/144	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00598 HarrisZ (more kpt) + DeepOrienta... Size: 512 bytes. Matches: custom	4478.94	563.62 Rank: 40/144	0.455 Rank: 127/144	0.711 Rank: 128/144	0.39767 (±0.00000) Rank: 123/144	576.83 Rank: 43/144	2707.54 Rank: 131/144	4.381 Rank: 66/144	0.445 Rank: 85/144	0.69090 (±0.00163) Rank: 75/144	0.54428 Rank: 112/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ (start scale 2) [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.5, degeneracy check true, confidence 0.98, max iter 500000) [Mishkin 2019]	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00082 LogPolar w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	496.36 Rank: 55/144	0.472 Rank: 102/144	0.817 Rank: 109/144	0.49129 (±0.00011) Rank: 84/144	399.70 Rank: 95/144	4036.37 Rank: 72/144	4.340 Rank: 77/144	0.429 Rank: 66/144	0.69100 (±0.00154) Rank: 73/144	0.59115 Rank: 83/144	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00610 Hardnet-Upright-AdaLAM Size: 512 bytes. Matches: custom	6556.61	627.71 Rank: 19/144	0.442 Rank: 130/144	0.828 Rank: 93/144	0.58300 (±0.00000) Rank: 12/144	645.47 Rank: 28/144	5074.91 Rank: 22/144	4.575 Rank: 30/144	0.361 Rank: 3/144	0.77056 (±0.00064) Rank: 3/144	0.67678 Rank: 4/144	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	Using upright Hardnet descriptors with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00568 Guided-SOSNet-lib-p Size: 512 bytes. Matches: custom	7829.63	508.45 Rank: 52/144	0.486 Rank: 45/144	0.874 Rank: 12/144	0.57982 (±0.00000) Rank: 16/144	524.66 Rank: 57/144	4618.86 Rank: 41/144	4.632 Rank: 16/144	0.369 Rank: 6/144	0.75888 (±0.00437) Rank: 10/144	0.66935 Rank: 9/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Zhongkun Chen, Jingchao Zhou, Pengfei Xu (contact)	sift8k	sosnet-lib-p (128 float32: 512 bytes)	sift and sosnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00117 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	299.02 Rank: 116/144	0.577 Rank: 6/144	0.831 Rank: 84/144	0.46793 (±0.00035) Rank: 101/144	350.69 Rank: 114/144	3327.71 Rank: 109/144	4.076 Rank: 124/144	0.487 Rank: 118/144	0.60691 (±0.00338) Rank: 123/144	0.53742 Rank: 114/144	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00139 CV-DoG-TFeat-kornia-PyRANSAC Size: 512 bytes. Matches: built-in	7860.77	160.77 Rank: 140/144	0.472 Rank: 82/144	0.839 Rank: 71/144	0.40079 (±0.00116) Rank: 119/144	265.53 Rank: 137/144	2905.25 Rank: 125/144	4.038 Rank: 128/144	0.488 Rank: 122/144	0.62608 (±0.00138) Rank: 119/144	0.51344 Rank: 123/144	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	21-02-05	is_baseline
Submission ID: 00567 Guided-HardNet32-v1-lib-qht-p Size: 512 bytes. Matches: custom	7829.63	520.40 Rank: 48/144	0.486 Rank: 45/144	0.875 Rank: 10/144	0.58509 (±0.00000) Rank: 10/144	536.93 Rank: 54/144	4685.16 Rank: 36/144	4.645 Rank: 12/144	0.376 Rank: 9/144	0.75702 (±0.00159) Rank: 12/144	0.67105 Rank: 7/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Zhongkun Chen, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet32-v1-lib-qht-p (128 float32: 512 bytes)	sift and sosnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00077 SOSNet-Upright w/ DEGENSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	508.38 Rank: 53/144	0.486 Rank: 45/144	0.877 Rank: 6/144	0.57385 (±0.00041) Rank: 18/144	600.23 Rank: 35/144	4765.43 Rank: 31/144	4.584 Rank: 27/144	0.403 Rank: 30/144	0.73027 (±0.00284) Rank: 25/144	0.65206 Rank: 24/144	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-23	is_baseline
Submission ID: 00101 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	598.28 Rank: 32/144	0.582 Rank: 2/144	0.826 Rank: 96/144	0.49856 (±0.00038) Rank: 81/144	356.21 Rank: 110/144	3366.01 Rank: 106/144	4.319 Rank: 81/144	0.464 Rank: 107/144	0.64829 (±0.00174) Rank: 105/144	0.57342 Rank: 95/144	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00556 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	385.16 Rank: 84/144	0.368 Rank: 135/144	0.646 Rank: 134/144	0.04192 (±0.00000) Rank: 141/144	392.52 Rank: 104/144	1475.19 Rank: 141/144	4.454 Rank: 55/144	0.601 Rank: 135/144	0.48498 (±0.00118) Rank: 135/144	0.26345 Rank: 139/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 5) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00524 SIFT-patch Size: 512 bytes. Matches: built-in	7861.62	84.08 Rank: 143/144	0.472 Rank: 99/144	0.574 Rank: 136/144	0.22805 (±0.00076) Rank: 133/144	81.93 Rank: 143/144	991.42 Rank: 143/144	1.934 Rank: 144/144	0.687 Rank: 140/144	0.08662 (±0.00384) Rank: 143/144	0.15733 Rank: 142/144	feyman_priv (contact)	sift8k	l2net-arcface (128 float32: 512 bytes)	sift8k with l2net trained on google-landmark-dataset-v1(in 1000 class)	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00546 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	1020.86 Rank: 6/144	0.368 Rank: 135/144	0.366 Rank: 140/144	0.07780 (±0.00000) Rank: 136/144	1047.01 Rank: 7/144	2197.09 Rank: 135/144	4.023 Rank: 131/144	0.509 Rank: 127/144	0.59222 (±0.00276) Rank: 127/144	0.33501 Rank: 131/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 15, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-02	is_baseline
Submission ID: 00635 MKDNet-indep Size: 512 bytes. Matches: built-in	7862.74	336.51 Rank: 107/144	0.472 Rank: 91/144	0.848 Rank: 50/144	0.51519 (±0.00069) Rank: 67/144	550.86 Rank: 48/144	4772.22 Rank: 30/144	4.265 Rank: 93/144	0.427 Rank: 62/144	0.70004 (±0.00374) Rank: 62/144	0.60762 Rank: 64/144	(contact)	sift	mkdnet-indep (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-13	is_submission
Submission ID: 00709 DISK (LCC/depth) Size: 512 bytes. Matches: built-in	7844.17	1238.52 Rank: 2/144	0.644 Rank: 1/144	0.852 Rank: 41/144	0.55847 (±0.00084) Rank: 27/144	1663.81 Rank: 1/144	7483.98 Rank: 2/144	5.922 Rank: 1/144	0.391 Rank: 23/144	0.75024 (±0.00316) Rank: 15/144	0.65435 Rank: 21/144	Under review! NeurIPS anonymous submission, ID 1194 (To be released: 21-06-02)	disk-cc-continued-20-imsize-1024-nms-3-nump-8000	disk-cc-continued-20-imsize-1024-nms-3-nump-8000 (128 float32: 512 bytes)	Local feature model learned via policy gradient. Model trained with a cycle-consistency loss and supervised with depth. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 3x3 window. We take the top 8000 features by score.	N/A	N/A	20-06-02	is_submission, is_challenge_2020, is_under_review
Submission ID: 00535 hardnetlib32P-Upright w/ DEGENSA... Size: 512 bytes. Matches: built-in	7829.63	527.64 Rank: 46/144	0.486 Rank: 45/144	0.876 Rank: 8/144	0.57327 (±0.00078) Rank: 20/144	506.73 Rank: 67/144	4230.98 Rank: 60/144	4.517 Rank: 48/144	0.418 Rank: 46/144	0.72171 (±0.00156) Rank: 39/144	0.64749 Rank: 28/144	Chen Shen, Zhipeng Wang, Jun Zhang, Jingchao Zhou, Pengfei Xu (contact)	sift8k-no-dups-mps	hardnetlib32P (128 float32: 512 bytes)	sift and hardnet with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: 00108 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	171.63 Rank: 137/144	0.482 Rank: 79/144	0.848 Rank: 48/144	0.39839 (±0.00122) Rank: 122/144	311.54 Rank: 134/144	3061.54 Rank: 118/144	4.105 Rank: 121/144	0.475 Rank: 116/144	0.62964 (±0.00428) Rank: 112/144	0.51401 Rank: 122/144	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00104 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	179.68 Rank: 136/144	0.490 Rank: 20/144	0.835 Rank: 80/144	0.39987 (±0.00068) Rank: 120/144	324.62 Rank: 129/144	3030.67 Rank: 121/144	4.173 Rank: 109/144	0.467 Rank: 111/144	0.62829 (±0.00112) Rank: 116/144	0.51408 Rank: 121/144	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00064 SOSNet w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	444.55 Rank: 67/144	0.472 Rank: 102/144	0.831 Rank: 85/144	0.51402 (±0.00057) Rank: 69/144	440.21 Rank: 80/144	4178.82 Rank: 61/144	4.378 Rank: 69/144	0.422 Rank: 55/144	0.70078 (±0.00375) Rank: 60/144	0.60740 Rank: 66/144	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00590 Guided-HardNet-epoch4 Size: 512 bytes. Matches: custom	7829.63	586.24 Rank: 34/144	0.486 Rank: 45/144	0.875 Rank: 9/144	0.59919 (±0.00000) Rank: 3/144	604.81 Rank: 34/144	5062.27 Rank: 23/144	4.710 Rank: 3/144	0.369 Rank: 7/144	0.76219 (±0.00253) Rank: 7/144	0.68069 Rank: 3/144	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch4 (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-24	is_submission, is_challenge_2020
Submission ID: 00506 sift and hardnet64 Size: 512 bytes. Matches: built-in	7861.62	358.55 Rank: 95/144	0.472 Rank: 100/144	0.849 Rank: 47/144	0.52085 (±0.00009) Rank: 60/144	585.33 Rank: 37/144	4957.86 Rank: 25/144	4.294 Rank: 87/144	0.419 Rank: 49/144	0.70472 (±0.00257) Rank: 56/144	0.61279 Rank: 55/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64 (128 float32: 512 bytes)	SIFT with 8000 keypoints, hardnet64 with 128 descriptors	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00116 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	209.26 Rank: 133/144	0.577 Rank: 6/144	0.841 Rank: 69/144	0.39330 (±0.00052) Rank: 124/144	350.69 Rank: 114/144	3327.71 Rank: 109/144	4.076 Rank: 124/144	0.490 Rank: 124/144	0.60691 (±0.00338) Rank: 123/144	0.50011 Rank: 126/144	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00085 LogPolar-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	611.19 Rank: 26/144	0.486 Rank: 45/144	0.830 Rank: 88/144	0.51287 (±0.00038) Rank: 71/144	483.93 Rank: 70/144	4405.57 Rank: 55/144	4.542 Rank: 42/144	0.423 Rank: 57/144	0.70680 (±0.00247) Rank: 53/144	0.60983 Rank: 62/144	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-23	is_baseline
Submission ID: 00109 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	250.11 Rank: 127/144	0.482 Rank: 79/144	0.838 Rank: 74/144	0.46795 (±0.00075) Rank: 100/144	311.54 Rank: 134/144	3061.54 Rank: 118/144	4.105 Rank: 121/144	0.475 Rank: 114/144	0.62964 (±0.00428) Rank: 112/144	0.54880 Rank: 106/144	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00060 L2-Net w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	386.81 Rank: 83/144	0.472 Rank: 102/144	0.813 Rank: 112/144	0.49054 (±0.00043) Rank: 87/144	314.54 Rank: 132/144	3314.19 Rank: 112/144	4.204 Rank: 107/144	0.465 Rank: 108/144	0.65665 (±0.00287) Rank: 99/144	0.57360 Rank: 94/144	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00542 sift and hardnet64 train scale(1... Size: 512 bytes. Matches: built-in	7830.09	607.44 Rank: 28/144	0.486 Rank: 33/144	0.872 Rank: 14/144	0.58801 (±0.00037) Rank: 6/144	950.74 Rank: 11/144	6286.36 Rank: 9/144	4.626 Rank: 17/144	0.390 Rank: 21/144	0.74662 (±0.00087) Rank: 19/144	0.66732 Rank: 12/144	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-l2 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00569 Upright-SIFT-HardNet Size: 512 bytes. Matches: built-in	6667.86	376.15 Rank: 89/144	0.491 Rank: 18/144	0.844 Rank: 62/144	0.51560 (±0.00043) Rank: 66/144	397.09 Rank: 97/144	3417.56 Rank: 105/144	4.479 Rank: 53/144	0.448 Rank: 92/144	0.68803 (±0.00532) Rank: 80/144	0.60181 Rank: 71/144	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-20	is_submission, is_challenge_2020
Submission ID: 00118 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	350.03 Rank: 99/144	0.577 Rank: 6/144	0.824 Rank: 100/144	0.46263 (±0.00079) Rank: 108/144	350.69 Rank: 114/144	3327.71 Rank: 109/144	4.076 Rank: 124/144	0.489 Rank: 123/144	0.60691 (±0.00338) Rank: 123/144	0.53477 Rank: 115/144	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00058 HardNet-Upright w/ MAGSAC (no FL... Size: 512 bytes. Matches: built-in	7829.63	707.86 Rank: 16/144	0.486 Rank: 45/144	0.853 Rank: 36/144	0.56374 (±0.00069) Rank: 24/144	509.07 Rank: 63/144	4250.40 Rank: 58/144	4.548 Rank: 39/144	0.412 Rank: 39/144	0.72309 (±0.00141) Rank: 35/144	0.64341 Rank: 29/144	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00131 CV-DoG-AffNet-HardNet-kornia-PyR... Size: 512 bytes. Matches: built-in	7833.97	267.89 Rank: 123/144	0.486 Rank: 74/144	0.890 Rank: 1/144	0.45050 (±0.00103) Rank: 113/144	580.47 Rank: 38/144	4671.35 Rank: 37/144	4.565 Rank: 33/144	0.403 Rank: 29/144	0.72668 (±0.00115) Rank: 30/144	0.58859 Rank: 84/144	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00549 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	216.23 Rank: 132/144	0.284 Rank: 140/144	0.456 Rank: 138/144	0.14544 (±0.00000) Rank: 135/144	221.40 Rank: 141/144	1839.87 Rank: 137/144	3.608 Rank: 136/144	0.641 Rank: 137/144	0.47073 (±0.00158) Rank: 137/144	0.30808 Rank: 135/144	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00607 HarrisZ (more kpt) + DeepOrienta... Size: 512 bytes. Matches: custom	4478.94	746.02 Rank: 14/144	0.455 Rank: 127/144	0.699 Rank: 129/144	0.40725 (±0.00000) Rank: 117/144	763.44 Rank: 24/144	2953.35 Rank: 124/144	4.414 Rank: 57/144	0.442 Rank: 77/144	0.69399 (±0.00179) Rank: 69/144	0.55062 Rank: 105/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ (start scale 2) [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true, confidence 0.98, max iter 500000) [Mishkin 2019]	N/A	N/A	20-06-03	is_submission, is_challenge_2020
Submission ID: 00112 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	204.37 Rank: 135/144	0.547 Rank: 12/144	0.823 Rank: 101/144	0.36954 (±0.00081) Rank: 126/144	347.39 Rank: 122/144	3209.10 Rank: 114/144	4.126 Rank: 117/144	0.517 Rank: 130/144	0.58657 (±0.00120) Rank: 129/144	0.47806 Rank: 128/144	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00545 structured feature description Size: 512 bytes. Matches: built-in	7830.21	411.68 Rank: 75/144	0.486 Rank: 43/144	0.865 Rank: 26/144	0.54442 (±0.00084) Rank: 38/144	524.05 Rank: 58/144	4467.23 Rank: 51/144	4.551 Rank: 38/144	0.412 Rank: 38/144	0.72729 (±0.00300) Rank: 29/144	0.63586 Rank: 33/144	Mahdi Abolfazli Esfahani, Han Wang (contact)	sift	sfd (128 float32: 512 bytes)	structured descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC	N/A	N/A	20-04-29	is_submission, is_challenge_2020
Submission ID: 00547 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	1078.85 Rank: 4/144	0.284 Rank: 140/144	0.166 Rank: 143/144	0.04936 (±0.00000) Rank: 140/144	1106.68 Rank: 5/144	2494.00 Rank: 133/144	3.400 Rank: 139/144	0.646 Rank: 138/144	0.43054 (±0.00185) Rank: 138/144	0.23995 Rank: 140/144	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 15, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00100 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	448.11 Rank: 66/144	0.582 Rank: 2/144	0.838 Rank: 73/144	0.39971 (±0.00147) Rank: 121/144	356.21 Rank: 110/144	3366.01 Rank: 106/144	4.319 Rank: 81/144	0.462 Rank: 102/144	0.64829 (±0.00174) Rank: 105/144	0.52400 Rank: 118/144	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline

Phototourism: restricted keypoints, standard descriptors (512 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00708 DISK (LCC/depth) Size: 512 bytes. Matches: built-in	2048.00	404.19 Rank: 7/94	0.448 Rank: 11/94	0.852 Rank: 1/94	0.51315 (±0.00028) Rank: 8/94	527.48 Rank: 2/94	2428.04 Rank: 3/94	5.545 Rank: 5/94	0.410 Rank: 11/94	0.72705 (±0.00094) Rank: 11/94	0.62010 Rank: 9/94	Michal Tyszkiewicz (contact)	disk-cc-continued-20-imsize-1024-nms-3-nump-2048	disk-cc-continued-20-imsize-1024-nms-3-nump-2048 (128 float32: 512 bytes)	Local feature model learned via policy gradient. Model trained with a cycle-consistency loss and supervised with depth. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 3x3 window. We take the top 2048 features by score.	N/A	N/A	20-06-03	is_submission, is_challenge_2020
Submission ID: 00608 SIFT2k_2048_HardNet64-train-all-... Size: 512 bytes. Matches: custom	2047.00	245.40 Rank: 22/94	0.346 Rank: 37/94	0.823 Rank: 6/94	0.49219 (±0.00000) Rank: 10/94	253.37 Rank: 40/94	1984.40 Rank: 16/94	4.607 Rank: 17/94	0.421 Rank: 15/94	0.70020 (±0.00213) Rank: 15/94	0.59619 Rank: 10/94	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	hardnet64-train-all-l2-138000-matched (128 float32: 512 bytes)	SIFT with 2048 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled, custom matches	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00702 L2-Net (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	117.11 Rank: 63/94	0.333 Rank: 51/94	0.808 Rank: 21/94	0.41918 (±0.00059) Rank: 42/94	179.79 Rank: 61/94	1361.34 Rank: 55/94	4.232 Rank: 57/94	0.481 Rank: 47/94	0.59682 (±0.00079) Rank: 56/94	0.50800 Rank: 47/94	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-06-01	is_baseline
Submission ID: 00663 disk_degree(patch)_End-to-End Size: 512 bytes. Matches: custom	2048.00	448.22 Rank: 3/94	0.447 Rank: 15/94	0.834 Rank: 4/94	0.53645 (±0.00000) Rank: 6/94	458.32 Rank: 5/94	2377.71 Rank: 4/94	5.657 Rank: 2/94	0.373 Rank: 5/94	0.75520 (±0.00336) Rank: 5/94	0.64582 Rank: 6/94	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree(patch)_End-to-End and DEGENSAC.	N/A	N/A	21-04-28	is_submission
Submission ID: 00653 sp_ae_sg_degensac_th Size: 512 bytes. Matches: custom	2048.00	293.72 Rank: 14/94	0.352 Rank: 33/94	0.772 Rank: 56/94	0.54380 (±0.00000) Rank: 5/94	300.66 Rank: 24/94	1801.43 Rank: 23/94	4.723 Rank: 13/94	0.366 Rank: 4/94	0.75988 (±0.00277) Rank: 4/94	0.65184 Rank: 5/94	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-14	is_submission
Submission ID: 00503 Guided matching hardnet Size: 512 bytes. Matches: custom	1892.71	149.75 Rank: 47/94	0.333 Rank: 65/94	0.795 Rank: 34/94	0.42159 (±0.00000) Rank: 40/94	254.13 Rank: 38/94	1754.99 Rank: 27/94	4.283 Rank: 53/94	0.464 Rank: 35/94	0.64144 (±0.00207) Rank: 35/94	0.53152 Rank: 38/94	(contact)	sift	upright-hardnet (128 float32: 512 bytes)	Sift keypoint ; upright hardnet descriptors ; custom matching: use a deep learning based coarse matcher as a first step. In a second step match the keypoint according to descriptor distances but only for the matches that are close to the coarse match prediction	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00703 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	1892.72	112.27 Rank: 71/94	0.333 Rank: 47/94	0.782 Rank: 44/94	0.39860 (±0.00077) Rank: 51/94	199.34 Rank: 52/94	1341.66 Rank: 57/94	4.090 Rank: 69/94	0.518 Rank: 64/94	0.56230 (±0.00234) Rank: 67/94	0.48045 Rank: 57/94	Challenge organizers (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-06-01	is_baseline
Submission ID: 00644 Example: Upright SIFT (OpenCV) Size: 512 bytes. Matches: built-in	1892.71	125.97 Rank: 61/94	0.333 Rank: 65/94	0.820 Rank: 11/94	0.43853 (±0.00030) Rank: 29/94	193.06 Rank: 57/94	1436.91 Rank: 46/94	4.278 Rank: 55/94	0.463 Rank: 33/94	0.63822 (±0.00469) Rank: 36/94	0.53838 Rank: 32/94	(contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	SIFT with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	https://opencv.org	21-03-13	is_submission
Submission ID: 00544 Key.Net + X-Net(Lib) w/ DEGENSAC Size: 512 bytes. Matches: built-in	2039.11	194.10 Rank: 28/94	0.447 Rank: 13/94	0.814 Rank: 15/94	0.42332 (±0.00096) Rank: 38/94	312.61 Rank: 20/94	1515.67 Rank: 39/94	4.567 Rank: 21/94	0.448 Rank: 24/94	0.66559 (±0.00233) Rank: 26/94	0.54445 Rank: 27/94	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-04-28	is_submission, is_challenge_2020
Submission ID: 00603 SuperPoint-128d-masked + SuperGl... Size: 512 bytes. Matches: custom	2041.09	404.73 Rank: 6/94	0.387 Rank: 25/94	0.774 Rank: 53/94	0.56769 (±0.00034) Rank: 3/94	415.75 Rank: 14/94	2158.03 Rank: 13/94	4.932 Rank: 10/94	0.357 Rank: 1/94	0.76987 (±0.00122) Rank: 3/94	0.66878 Rank: 3/94	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms3-refine2-r1600forcecubic-masked-d.001	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 3, confidence threshold 0.001, refinement, on 1600-pixel images). Detections on semantic classes sky and people are removed (segmentation from HFNetV2 trained on MIT ADE20K). SuperPoint descriptor, reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.2 pixel inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-30	is_submission, is_challenge_2020
Submission ID: 00622 Sift-HardNet-NM-Net_End-to-End Size: 512 bytes. Matches: custom	1892.70	152.13 Rank: 44/94	0.333 Rank: 67/94	0.807 Rank: 24/94	0.46620 (±0.00000) Rank: 14/94	156.54 Rank: 75/94	1403.97 Rank: 49/94	4.425 Rank: 32/94	0.443 Rank: 22/94	0.67160 (±0.00184) Rank: 24/94	0.56890 Rank: 18/94	Chen Zhao (contact)	siftdef	hardnet (128 float32: 512 bytes)	SIFT and HardNet, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00668 sp_ae_sg_degensac_plus Size: 512 bytes. Matches: custom	2048.00	394.88 Rank: 8/94	0.430 Rank: 19/94	0.749 Rank: 70/94	0.37510 (±0.00001) Rank: 64/94	402.56 Rank: 15/94	2246.23 Rank: 7/94	5.344 Rank: 7/94	0.432 Rank: 19/94	0.69832 (±0.00287) Rank: 17/94	0.53671 Rank: 35/94	(contact)	sid-00668-superpoint	sid-00668-superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-05-03	is_submission
Submission ID: 00667 sp_ae_sg_degensac_xp Size: 512 bytes. Matches: custom	2048.00	446.24 Rank: 4/94	0.407 Rank: 22/94	0.791 Rank: 37/94	0.58918 (±0.00001) Rank: 2/94	458.12 Rank: 6/94	2290.85 Rank: 6/94	5.123 Rank: 8/94	0.365 Rank: 3/94	0.77685 (±0.00244) Rank: 1/94	0.68301 Rank: 1/94	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-29	is_submission
Submission ID: 00023 LogPolarDesc, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	116.83 Rank: 64/94	0.323 Rank: 75/94	0.770 Rank: 57/94	0.39335 (±0.00060) Rank: 53/94	162.84 Rank: 71/94	1385.60 Rank: 53/94	4.046 Rank: 72/94	0.519 Rank: 65/94	0.57149 (±0.00344) Rank: 63/94	0.48242 Rank: 55/94	Challenge organizers (contact)	sift-def	logpolar (128 float32: 512 bytes)	LogPolarDesc descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00583 R2D2 Size: 512 bytes. Matches: built-in	2048.00	169.38 Rank: 34/94	0.398 Rank: 24/94	0.695 Rank: 77/94	0.33715 (±0.00149) Rank: 74/94	306.15 Rank: 21/94	1235.83 Rank: 73/94	4.156 Rank: 67/94	0.496 Rank: 51/94	0.60223 (±0.00103) Rank: 53/94	0.46969 Rank: 68/94	(contact)	r2d2-5k-p-aug	r2d2-5k-p-aug (128 float32: 512 bytes)		N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00001 HardNet, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	109.52 Rank: 74/94	0.323 Rank: 75/94	0.776 Rank: 50/94	0.38578 (±0.00131) Rank: 59/94	153.29 Rank: 77/94	1306.64 Rank: 62/94	4.026 Rank: 76/94	0.539 Rank: 77/94	0.55573 (±0.00175) Rank: 73/94	0.47075 Rank: 66/94	Challenge organizers (contact)	sift-def	hardnet (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00585 NM-Net_v2 Size: 512 bytes. Matches: custom	1892.70	151.46 Rank: 46/94	0.333 Rank: 67/94	0.809 Rank: 19/94	0.46620 (±0.00000) Rank: 13/94	155.91 Rank: 76/94	1392.47 Rank: 52/94	4.424 Rank: 33/94	0.459 Rank: 30/94	0.66837 (±0.00125) Rank: 25/94	0.56729 Rank: 19/94	Chen Zhao (contact)	siftdef	hardnet (128 float32: 512 bytes)	SIFT and HardNet, followed by NM-Net_v2 and DEGENSAC.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00589 SuperPoint-128d + SuperGlue + DE... Size: 512 bytes. Matches: custom	1973.62	320.46 Rank: 13/94	0.364 Rank: 30/94	0.772 Rank: 55/94	0.55214 (±0.00098) Rank: 4/94	429.49 Rank: 13/94	2130.73 Rank: 14/94	4.570 Rank: 19/94	0.384 Rank: 7/94	0.75283 (±0.00086) Rank: 7/94	0.65248 Rank: 4/94	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms3-refine2-r1600forcecubic	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 3, refinement, on 1600-pixel images) and descriptor; reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.2 pixels inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-24	is_submission, is_challenge_2020
Submission ID: 00617 pffNet + SuperPoint + DEGENSAC Size: 512 bytes. Matches: built-in	1267.22	55.58 Rank: 93/94	0.341 Rank: 38/94	0.629 Rank: 84/94	0.25514 (±0.00059) Rank: 82/94	131.91 Rank: 88/94	893.88 Rank: 91/94	4.339 Rank: 37/94	0.534 Rank: 73/94	0.55935 (±0.00143) Rank: 71/94	0.40724 Rank: 80/94	Jongmin Lee, Seungwook Kim, Yoonwoo Jeong (contact)	superpoint	pffnet (128 float32: 512 bytes)	pffNet descriptors+ SuperPoint keypoints + DEGENSAC outlier-filtering	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00123 KeyNet-HardNet-2k Size: 512 bytes. Matches: built-in	2048.00	134.39 Rank: 56/94	0.469 Rank: 1/94	0.818 Rank: 12/94	0.32725 (±0.00026) Rank: 77/94	195.33 Rank: 53/94	1276.30 Rank: 68/94	4.493 Rank: 28/94	0.490 Rank: 50/94	0.61606 (±0.00141) Rank: 47/94	0.47166 Rank: 65/94	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-03	is_baseline
Submission ID: 00008 L2-Net (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	138.44 Rank: 53/94	0.333 Rank: 51/94	0.786 Rank: 40/94	0.39980 (±0.00020) Rank: 50/94	171.93 Rank: 64/94	1333.56 Rank: 59/94	4.226 Rank: 59/94	0.496 Rank: 52/94	0.58762 (±0.00228) Rank: 57/94	0.49371 Rank: 54/94	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-22	is_baseline
Submission ID: 00647 sp_ae_sg_ransac Size: 512 bytes. Matches: custom	1873.96	252.53 Rank: 19/94	0.323 Rank: 72/94	0.663 Rank: 81/94	0.06318 (±0.00000) Rank: 93/94	256.66 Rank: 36/94	1663.09 Rank: 31/94	4.771 Rank: 11/94	0.563 Rank: 82/94	0.52605 (±0.00451) Rank: 78/94	0.29461 Rank: 86/94	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-03-29	is_submission
Submission ID: 00646 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	241.83 Rank: 24/94	0.450 Rank: 4/94	0.689 Rank: 79/94	0.32109 (±0.00043) Rank: 79/94	301.64 Rank: 22/94	1593.08 Rank: 35/94	4.285 Rank: 51/94	0.497 Rank: 54/94	0.60774 (±0.00266) Rank: 49/94	0.46442 Rank: 71/94	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features score-wise. HardNet, pre-trained on all datasets, is used as a descriptor head. Graph-Cut(GC)-RANSAC is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-28	is_submission
Submission ID: 00509 Upright-Sift + X-Net-lib w/ DEGE... Size: 512 bytes. Matches: built-in	1892.70	116.55 Rank: 67/94	0.333 Rank: 71/94	0.821 Rank: 9/94	0.42382 (±0.00089) Rank: 37/94	168.67 Rank: 67/94	1319.63 Rank: 61/94	4.303 Rank: 47/94	0.468 Rank: 40/94	0.62953 (±0.00216) Rank: 42/94	0.52668 Rank: 41/94	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	sift-def	x-net-lib-upright-no-dups (128 float32: 512 bytes)		N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00658 disk_degree_end-to-end Size: 512 bytes. Matches: custom	2048.00	346.06 Rank: 11/94	0.447 Rank: 15/94	0.843 Rank: 2/94	0.51061 (±0.00000) Rank: 9/94	354.06 Rank: 17/94	2170.03 Rank: 10/94	5.679 Rank: 1/94	0.393 Rank: 8/94	0.74934 (±0.00266) Rank: 8/94	0.62998 Rank: 8/94	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree_End-to-End and DEGENSAC.	N/A	N/A	21-04-22	is_submission
Submission ID: 00605 guided-hardnet-epoch2-v2 Size: 512 bytes. Matches: custom	2047.76	220.88 Rank: 25/94	0.347 Rank: 34/94	0.778 Rank: 48/94	0.46469 (±0.00000) Rank: 15/94	226.84 Rank: 44/94	1827.60 Rank: 20/94	4.512 Rank: 25/94	0.427 Rank: 17/94	0.69306 (±0.00160) Rank: 19/94	0.57888 Rank: 16/94	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet and guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00645 sp_ae_sg Size: 512 bytes. Matches: custom	1873.96	488.91 Rank: 2/94	0.323 Rank: 72/94	0.585 Rank: 86/94	0.28532 (±0.00000) Rank: 81/94	498.60 Rank: 3/94	2432.22 Rank: 2/94	4.542 Rank: 23/94	0.399 Rank: 10/94	0.72857 (±0.00361) Rank: 10/94	0.50694 Rank: 48/94	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-03-18	is_submission
Submission ID: 00527 SEKD Size: 512 bytes. Matches: built-in	1786.69	106.34 Rank: 76/94	0.383 Rank: 28/94	0.759 Rank: 66/94	0.38886 (±0.00013) Rank: 54/94	150.11 Rank: 79/94	1063.08 Rank: 88/94	4.575 Rank: 18/94	0.465 Rank: 37/94	0.63303 (±0.00244) Rank: 40/94	0.51095 Rank: 46/94	Yafei Song, Ling Cai, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	The name of our method is Self-Evolving Keypoint Detection and Description (SEKD). Now, the SEKD model is trained only using COCO test images. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: ????? CV-DoG-HardNet8-PT Size: 512 bytes. Matches: built-in	2048.00	114.84 Rank: 70/94	0.321 Rank: 88/94	0.790 Rank: 38/94	0.38200 (±0.00081) Rank: 61/94	117.84 Rank: 94/94	1067.13 Rank: 87/94	4.051 Rank: 71/94	0.531 Rank: 71/94	0.53035 (±0.00531) Rank: 76/94	0.45617 Rank: 73/94	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift2k	h8e512pt (128 float32: 512 bytes)	[sid:00593] HardNet8 with PCA compression	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00044 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1892.72	104.22 Rank: 79/94	0.333 Rank: 47/94	0.763 Rank: 63/94	0.37078 (±0.00056) Rank: 66/94	205.17 Rank: 48/94	1300.49 Rank: 64/94	4.000 Rank: 81/94	0.563 Rank: 81/94	0.52549 (±0.00120) Rank: 79/94	0.44814 Rank: 75/94	Challenge organizers (contact)	sift-def	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00004 SOSNet (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	176.54 Rank: 30/94	0.333 Rank: 51/94	0.782 Rank: 43/94	0.43803 (±0.00092) Rank: 31/94	183.92 Rank: 58/94	1403.66 Rank: 50/94	4.314 Rank: 43/94	0.474 Rank: 45/94	0.62181 (±0.00161) Rank: 45/94	0.52992 Rank: 40/94	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00043 DELF-GLD (128D), PyRANSAC Size: 512 bytes. Matches: built-in	2036.82	94.70 Rank: 82/94	0.109 Rank: 90/94	0.156 Rank: 90/94	0.07298 (±0.00056) Rank: 91/94	430.68 Rank: 11/94	2162.72 Rank: 11/94	2.535 Rank: 90/94	0.887 Rank: 91/94	0.13247 (±0.00116) Rank: 89/94	0.10272 Rank: 90/94	Challenge organizers (contact)	delf-gld-2k-128d	delf-gld-2k-128d (128 float32: 512 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 128 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-23	is_baseline
Submission ID: 00612 SuperPoint-128d-adapt + SuperGlu... Size: 512 bytes. Matches: custom	2048.00	441.49 Rank: 5/94	0.407 Rank: 21/94	0.789 Rank: 39/94	0.59034 (±0.00050) Rank: 1/94	452.99 Rank: 9/94	2245.43 Rank: 8/94	5.092 Rank: 9/94	0.358 Rank: 2/94	0.77337 (±0.00167) Rank: 2/94	0.68186 Rank: 2/94	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms4-refine2-r1600forcecubic-masked-d.001-adapt50	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 4, confidence threshold 0.001, refinement, on 1600-pixel images). The detection heatmap is improved with test-time homographic adaptation (50 iterations), and detections on semantic classes sky and people are removed (segmentation from HFNetV2 trained on MIT ADE20K). SuperPoint descriptor, reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.1 pixel inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-31	is_submission, is_challenge_2020
Submission ID: 00615 SIFT-Fusion_Max-NM-Net_End-to-En... Size: 512 bytes. Matches: custom	1892.70	115.46 Rank: 68/94	0.333 Rank: 67/94	0.783 Rank: 42/94	0.40244 (±0.00000) Rank: 48/94	119.23 Rank: 93/94	1167.54 Rank: 80/94	4.367 Rank: 35/94	0.489 Rank: 49/94	0.60720 (±0.00114) Rank: 50/94	0.50482 Rank: 49/94	Chen Zhao (contact)	siftdef	fusion-max (128 float32: 512 bytes)	SIFT and Fusion_Max, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00002 SURF (OpenCV), DEGENSAC Size: 256 bytes. Matches: built-in	2010.88	71.90 Rank: 88/94	0.321 Rank: 87/94	0.634 Rank: 82/94	0.20761 (±0.00029) Rank: 85/94	300.02 Rank: 25/94	1208.06 Rank: 78/94	3.549 Rank: 87/94	0.695 Rank: 87/94	0.37845 (±0.00399) Rank: 87/94	0.29303 Rank: 87/94	Challenge organizers (contact)	surf-def	surf (64 float32: 256 bytes)	SURF with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00641 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	219.72 Rank: 26/94	0.450 Rank: 4/94	0.722 Rank: 75/94	0.34184 (±0.00032) Rank: 72/94	301.64 Rank: 22/94	1595.30 Rank: 34/94	4.278 Rank: 54/94	0.499 Rank: 55/94	0.60963 (±0.00152) Rank: 48/94	0.47574 Rank: 61/94	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. MAGSAC is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-04	is_submission
Submission ID: 00705 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1892.72	104.74 Rank: 78/94	0.333 Rank: 47/94	0.764 Rank: 62/94	0.37292 (±0.00081) Rank: 65/94	204.13 Rank: 49/94	1293.75 Rank: 65/94	4.008 Rank: 78/94	0.567 Rank: 83/94	0.52808 (±0.00416) Rank: 77/94	0.45050 Rank: 74/94	Challenge organizers (contact)	sift-def	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-06-01	is_baseline
Submission ID: 00014 L2-Net, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	107.38 Rank: 75/94	0.323 Rank: 81/94	0.766 Rank: 59/94	0.35649 (±0.00110) Rank: 70/94	138.97 Rank: 86/94	1211.49 Rank: 75/94	3.948 Rank: 82/94	0.538 Rank: 76/94	0.52019 (±0.00120) Rank: 80/94	0.43834 Rank: 77/94	Challenge organizers (contact)	sift-def	l2net (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00032 Root-SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1936.30	83.46 Rank: 86/94	0.323 Rank: 85/94	0.756 Rank: 68/94	0.33605 (±0.00092) Rank: 75/94	167.84 Rank: 68/94	1141.88 Rank: 83/94	3.788 Rank: 85/94	0.576 Rank: 84/94	0.46826 (±0.00154) Rank: 84/94	0.40216 Rank: 81/94	Challenge organizers (contact)	sift-def	rootsift (128 float32: 512 bytes)	Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00007 L2-Net, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	87.04 Rank: 85/94	0.323 Rank: 81/94	0.779 Rank: 47/94	0.36265 (±0.00090) Rank: 67/94	138.97 Rank: 86/94	1211.49 Rank: 75/94	3.948 Rank: 82/94	0.537 Rank: 74/94	0.52019 (±0.00120) Rank: 80/94	0.44142 Rank: 76/94	Challenge organizers (contact)	sift-def	l2net (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00599 SEKD Size: 512 bytes. Matches: built-in	2043.44	129.46 Rank: 59/94	0.386 Rank: 26/94	0.809 Rank: 20/94	0.45066 (±0.00057) Rank: 22/94	176.60 Rank: 62/94	1209.63 Rank: 77/94	4.437 Rank: 30/94	0.454 Rank: 27/94	0.66094 (±0.00227) Rank: 28/94	0.55580 Rank: 23/94	Yafei Song, Ling Cai, Jia Li, Yonghong Tian, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	Our method named SEKD: Self-Evolving Keypoint Detection and Description, where the SEKD model is trained using COCO validation set. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, without flann, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-05-29	is_submission, is_challenge_2020
Submission ID: 00136 CV-DoG-AffNet-HardNet-kornia-MAG... Size: 512 bytes. Matches: built-in	2047.84	195.17 Rank: 27/94	0.339 Rank: 40/94	0.774 Rank: 52/94	0.41747 (±0.00072) Rank: 43/94	284.06 Rank: 30/94	1788.70 Rank: 24/94	4.191 Rank: 63/94	0.511 Rank: 61/94	0.58536 (±0.00125) Rank: 59/94	0.50141 Rank: 51/94	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00011 GeoDesc, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	93.44 Rank: 83/94	0.323 Rank: 81/94	0.759 Rank: 65/94	0.34215 (±0.00054) Rank: 71/94	120.68 Rank: 91/94	1098.70 Rank: 85/94	4.002 Rank: 79/94	0.552 Rank: 80/94	0.50594 (±0.00073) Rank: 82/94	0.42405 Rank: 78/94	Challenge organizers (contact)	sift-def	geodesc (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00018 R2D2 (r2d2-wasf-n16), DEGENSAC Size: 512 bytes. Matches: built-in	2048.00	176.49 Rank: 31/94	0.422 Rank: 20/94	0.725 Rank: 74/94	0.35783 (±0.00061) Rank: 69/94	262.02 Rank: 35/94	1188.11 Rank: 79/94	4.299 Rank: 50/94	0.499 Rank: 56/94	0.60639 (±0.00124) Rank: 51/94	0.48211 Rank: 56/94	Challenge organizers (contact)	r2d2-wasf-n16	r2d2-wasf-n16 (128 float32: 512 bytes)	R2D2 (model r2d2-wasf-n16) with 2k features, using the built-in matcher (bidirectional filter with the both strategy) and no ratio test, and DEGENSAC with optimal parameters for stereo.	N/A	https://opencv.org	20-04-23	is_baseline
Submission ID: ????? CV-DoG-HardNet8-PTv2 Size: 512 bytes. Matches: built-in	2047.77	153.69 Rank: 42/94	0.339 Rank: 44/94	0.821 Rank: 8/94	0.43869 (±0.00017) Rank: 28/94	158.63 Rank: 74/94	1221.12 Rank: 74/94	4.350 Rank: 36/94	0.484 Rank: 48/94	0.60206 (±0.00003) Rank: 54/94	0.52038 Rank: 43/94	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift2k	h8e512pt (128 float32: 512 bytes)	[sid:00619] HardNet8 with PCA compression, batch sampling from few images	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00025 DELF-GLD (32D), DEGENSAC Size: 128 bytes. Matches: built-in	2036.82	96.56 Rank: 81/94	0.109 Rank: 90/94	0.119 Rank: 93/94	0.06573 (±0.00030) Rank: 92/94	453.19 Rank: 7/94	1805.48 Rank: 21/94	2.425 Rank: 92/94	0.920 Rank: 94/94	0.09163 (±0.00140) Rank: 92/94	0.07868 Rank: 92/94	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00578 Key.Net-s + DescNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	2033.67	246.62 Rank: 21/94	0.449 Rank: 9/94	0.805 Rank: 25/94	0.45418 (±0.00056) Rank: 20/94	331.55 Rank: 19/94	1621.68 Rank: 33/94	4.570 Rank: 20/94	0.447 Rank: 23/94	0.67414 (±0.00371) Rank: 23/94	0.56416 Rank: 20/94	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet-s	descnet (128 float32: 512 bytes)		N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00016 LogPolarDesc, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	148.56 Rank: 48/94	0.323 Rank: 75/94	0.752 Rank: 69/94	0.38840 (±0.00064) Rank: 55/94	162.84 Rank: 71/94	1385.60 Rank: 53/94	4.046 Rank: 72/94	0.516 Rank: 63/94	0.57149 (±0.00344) Rank: 63/94	0.47994 Rank: 58/94	Challenge organizers (contact)	sift-def	logpolar (128 float32: 512 bytes)	LogPolarDesc descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-23	is_baseline
Submission ID: 00704 SOSNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	171.22 Rank: 33/94	0.333 Rank: 51/94	0.804 Rank: 26/94	0.45053 (±0.00053) Rank: 23/94	194.04 Rank: 54/94	1442.32 Rank: 45/94	4.313 Rank: 45/94	0.467 Rank: 39/94	0.63586 (±0.00246) Rank: 38/94	0.54320 Rank: 28/94	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-06-02	is_baseline
Submission ID: 00604 Key.Net+GIFT+GMCNet+DEGENSAC Size: 512 bytes. Matches: custom	2046.07	377.20 Rank: 9/94	0.450 Rank: 8/94	0.776 Rank: 49/94	0.45282 (±0.00000) Rank: 21/94	386.63 Rank: 16/94	1474.77 Rank: 42/94	4.663 Rank: 16/94	0.420 Rank: 13/94	0.70500 (±0.00294) Rank: 12/94	0.57891 Rank: 15/94	Yuan Liu (contact)	keynet-2k	scale-gift (128 float32: 512 bytes)	Detecting by Key.Net, descriptors from GIFT, matching by Graph Motion Coherence Network, geometry estimated by DEGENSAC with inlier threshold 0.7	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00700 HardNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	152.69 Rank: 43/94	0.333 Rank: 51/94	0.812 Rank: 17/94	0.46093 (±0.00080) Rank: 17/94	201.26 Rank: 50/94	1467.86 Rank: 44/94	4.311 Rank: 46/94	0.466 Rank: 38/94	0.63544 (±0.00324) Rank: 39/94	0.54819 Rank: 25/94	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-06-01	is_baseline
Submission ID: 00036 SOSNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	155.75 Rank: 40/94	0.333 Rank: 51/94	0.794 Rank: 35/94	0.43077 (±0.00017) Rank: 34/94	183.92 Rank: 58/94	1403.66 Rank: 50/94	4.314 Rank: 43/94	0.472 Rank: 42/94	0.62181 (±0.00161) Rank: 45/94	0.52629 Rank: 42/94	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00575 SIFT2k-2048-HardNet64-rain-all-s... Size: 512 bytes. Matches: built-in	2047.77	161.77 Rank: 36/94	0.339 Rank: 43/94	0.814 Rank: 13/94	0.43543 (±0.00051) Rank: 32/94	255.45 Rank: 37/94	1708.29 Rank: 28/94	4.327 Rank: 41/94	0.451 Rank: 26/94	0.64653 (±0.00221) Rank: 31/94	0.54098 Rank: 29/94	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2048-hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 2048 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00531 Key.Net + X-Net w/ DEGENSAC Size: 512 bytes. Matches: built-in	2040.08	173.65 Rank: 32/94	0.447 Rank: 12/94	0.797 Rank: 33/94	0.38608 (±0.00068) Rank: 58/94	276.91 Rank: 34/94	1468.50 Rank: 43/94	4.447 Rank: 29/94	0.463 Rank: 34/94	0.64369 (±0.00081) Rank: 34/94	0.51489 Rank: 45/94	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet	x-net (128 float32: 512 bytes)		N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00565 SIFT2k_2000_HardNet64-train-all-... Size: 512 bytes. Matches: built-in	1999.83	157.70 Rank: 38/94	0.336 Rank: 45/94	0.813 Rank: 16/94	0.43188 (±0.00005) Rank: 33/94	248.91 Rank: 42/94	1666.27 Rank: 30/94	4.323 Rank: 42/94	0.465 Rank: 36/94	0.64404 (±0.00271) Rank: 33/94	0.53796 Rank: 33/94	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2000-hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 2000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00512 Hardnet with DEGENSAC for stereo Size: 512 bytes. Matches: built-in	1999.50	51.91 Rank: 94/94	0.172 Rank: 89/94	0.558 Rank: 88/94	0.20771 (±0.00049) Rank: 84/94	181.48 Rank: 60/94	1692.63 Rank: 29/94	3.050 Rank: 89/94	0.695 Rank: 86/94	0.38463 (±0.00275) Rank: 86/94	0.29617 Rank: 85/94	Vu Trung Nghia & Nguyen Trung Hieu (contact)	siftdef	hardnet-64 (128 float32: 512 bytes)	Using hardnet network to embed a patch (32 x 32) to a 128 (float32) dimensions vector, For stereo, we use DEGENSAC (Chum et al, CVPR'05) with optimal settings	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00012 GeoDesc (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	144.92 Rank: 49/94	0.333 Rank: 51/94	0.765 Rank: 61/94	0.38124 (±0.00056) Rank: 62/94	149.66 Rank: 80/94	1237.96 Rank: 71/94	4.219 Rank: 61/94	0.526 Rank: 70/94	0.56687 (±0.00261) Rank: 65/94	0.47405 Rank: 62/94	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00038 SOSNet, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	134.59 Rank: 55/94	0.323 Rank: 75/94	0.760 Rank: 64/94	0.38654 (±0.00056) Rank: 57/94	145.47 Rank: 84/94	1271.41 Rank: 69/94	4.045 Rank: 74/94	0.526 Rank: 69/94	0.56072 (±0.00077) Rank: 69/94	0.47363 Rank: 63/94	Challenge organizers (contact)	sift-def	sosnet (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00582 R2D2 Size: 512 bytes. Matches: built-in	2048.00	155.21 Rank: 41/94	0.403 Rank: 23/94	0.709 Rank: 76/94	0.32501 (±0.00085) Rank: 78/94	285.11 Rank: 29/94	1162.94 Rank: 82/94	4.161 Rank: 66/94	0.508 Rank: 58/94	0.60276 (±0.00062) Rank: 52/94	0.46389 Rank: 72/94	(contact)	r2d2-5k-p	r2d2-5k-p (128 float32: 512 bytes)		N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00555 SuperPoint+GIFT+Graph Motion Coh... Size: 512 bytes. Matches: custom	1940.85	281.06 Rank: 16/94	0.356 Rank: 31/94	0.676 Rank: 80/94	0.42283 (±0.00000) Rank: 39/94	290.06 Rank: 27/94	1887.70 Rank: 19/94	4.567 Rank: 22/94	0.420 Rank: 14/94	0.70395 (±0.00239) Rank: 13/94	0.56339 Rank: 21/94	Yuan Liu (contact)	superpoint-2k	scale-gift (128 float32: 512 bytes)	Detecting by SuperPoint, descriptors from GIFT, matching by Graph Motion Coherence Network, geometry estimated by DEGENSAC with inlier threshold 1.0	N/A	N/A	20-05-10	is_submission, is_challenge_2020
Submission ID: 00640 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	128.76 Rank: 60/94	0.450 Rank: 4/94	0.744 Rank: 71/94	0.33122 (±0.00042) Rank: 76/94	289.47 Rank: 28/94	1543.70 Rank: 38/94	4.284 Rank: 52/94	0.509 Rank: 59/94	0.60128 (±0.00311) Rank: 55/94	0.46625 Rank: 70/94	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. DEGENSA with degeneracy check on is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-02-17	is_submission
Submission ID: 00039 HardNet, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	138.43 Rank: 54/94	0.323 Rank: 75/94	0.757 Rank: 67/94	0.38490 (±0.00120) Rank: 60/94	153.29 Rank: 77/94	1306.64 Rank: 62/94	4.026 Rank: 76/94	0.537 Rank: 75/94	0.55573 (±0.00175) Rank: 73/94	0.47032 Rank: 67/94	Challenge organizers (contact)	sift-def	hardnet (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00516 Key.Net (MS 4-1, Pyramid) + X-Ne... Size: 512 bytes. Matches: built-in	2039.11	265.52 Rank: 18/94	0.447 Rank: 13/94	0.776 Rank: 51/94	0.40559 (±0.00112) Rank: 46/94	336.81 Rank: 18/94	1559.74 Rank: 36/94	4.536 Rank: 24/94	0.461 Rank: 32/94	0.66406 (±0.00521) Rank: 27/94	0.53483 Rank: 36/94	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet-41-pyramidv2	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00517 retrained Hardnet with MAGSAC Size: 512 bytes. Matches: built-in	1892.71	133.39 Rank: 57/94	0.333 Rank: 51/94	0.808 Rank: 22/94	0.40042 (±0.00077) Rank: 49/94	210.10 Rank: 47/94	1480.67 Rank: 41/94	4.226 Rank: 58/94	0.479 Rank: 46/94	0.63030 (±0.00242) Rank: 41/94	0.51536 Rank: 44/94	Vu Trung Nghia and Nguyen Trung Hieu (contact)	sift-upright	hardnet (128 float32: 512 bytes)	Using SIFT upright and retrained Hardnet for keypoints and descriptors. For stereo, we use DEGENSAC Chum et al, CVPR05 with optimal settings	N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00711 DISK-32D (depth) Size: 128 bytes. Matches: built-in	2048.00	341.83 Rank: 12/94	0.448 Rank: 10/94	0.842 Rank: 3/94	0.47005 (±0.00037) Rank: 11/94	441.16 Rank: 10/94	2231.55 Rank: 9/94	5.524 Rank: 6/94	0.415 Rank: 12/94	0.70220 (±0.00208) Rank: 14/94	0.58613 Rank: 12/94	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with depth-based constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 7x7 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00009 GeoDesc (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	116.56 Rank: 66/94	0.333 Rank: 51/94	0.784 Rank: 41/94	0.38660 (±0.00078) Rank: 56/94	149.66 Rank: 80/94	1237.96 Rank: 71/94	4.219 Rank: 61/94	0.525 Rank: 68/94	0.56687 (±0.00261) Rank: 65/94	0.47673 Rank: 60/94	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00623 HardNet-epoch2-OANet Size: 512 bytes. Matches: custom	2047.76	247.45 Rank: 20/94	0.347 Rank: 34/94	0.765 Rank: 60/94	0.44717 (±0.00000) Rank: 25/94	254.11 Rank: 39/94	1898.45 Rank: 18/94	4.503 Rank: 26/94	0.437 Rank: 21/94	0.67958 (±0.00183) Rank: 21/94	0.56337 Rank: 22/94	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet trained from scratch and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00620 pffNet + SuperPoint + MAGSAC Size: 512 bytes. Matches: built-in	1267.22	57.48 Rank: 92/94	0.341 Rank: 38/94	0.632 Rank: 83/94	0.22549 (±0.00077) Rank: 83/94	131.91 Rank: 88/94	893.88 Rank: 91/94	4.339 Rank: 37/94	0.531 Rank: 72/94	0.55935 (±0.00143) Rank: 71/94	0.39242 Rank: 82/94	Jongmin Lee, Seungwook Kim, Yoonwoo Jeong (contact)	superpoint	pffnet (128 float32: 512 bytes)	pffNet descriptors + SuperPoint keypoints + MAGSAC outlier-filtering	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00135 CV-DoG-AffNet-HardNet-kornia-PyR... Size: 512 bytes. Matches: built-in	2047.84	105.57 Rank: 77/94	0.339 Rank: 40/94	0.800 Rank: 30/94	0.35887 (±0.00090) Rank: 68/94	284.06 Rank: 30/94	1788.70 Rank: 24/94	4.191 Rank: 63/94	0.513 Rank: 62/94	0.58536 (±0.00125) Rank: 59/94	0.47211 Rank: 64/94	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00026 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	1657.83	69.77 Rank: 90/94	0.355 Rank: 32/94	0.622 Rank: 85/94	0.17632 (±0.00087) Rank: 87/94	224.40 Rank: 45/94	795.54 Rank: 93/94	3.311 Rank: 88/94	0.819 Rank: 88/94	0.23020 (±0.00466) Rank: 88/94	0.20326 Rank: 88/94	Challenge organizers (contact)	akaze-def	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00706 LogPolar-Upright w/ DEGENSAC (no... Size: 512 bytes. Matches: built-in	1892.71	162.20 Rank: 35/94	0.333 Rank: 51/94	0.807 Rank: 23/94	0.45674 (±0.00059) Rank: 19/94	211.87 Rank: 46/94	1553.36 Rank: 37/94	4.331 Rank: 40/94	0.471 Rank: 41/94	0.63701 (±0.00278) Rank: 37/94	0.54688 Rank: 26/94	Challenge organizers (contact)	sift-def	logpolar-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV). Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-06-02	is_baseline
Submission ID: 00669 disk_degree(refine)_End-to-End Size: 512 bytes. Matches: custom	2048.00	591.03 Rank: 1/94	0.447 Rank: 15/94	0.811 Rank: 18/94	0.53291 (±0.00000) Rank: 7/94	602.70 Rank: 1/94	2633.91 Rank: 1/94	5.556 Rank: 4/94	0.382 Rank: 6/94	0.75413 (±0.00231) Rank: 6/94	0.64352 Rank: 7/94	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree(refine)_End-to-End and DEGENSAC.	N/A	N/A	21-05-07	is_submission
Submission ID: 00022 L2-Net (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	110.72 Rank: 73/94	0.333 Rank: 51/94	0.801 Rank: 29/94	0.40729 (±0.00097) Rank: 45/94	171.93 Rank: 64/94	1333.56 Rank: 59/94	4.226 Rank: 59/94	0.497 Rank: 53/94	0.58762 (±0.00228) Rank: 57/94	0.49746 Rank: 53/94	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00606 HardNet-epoch2-OANet Size: 512 bytes. Matches: custom	2047.76	244.62 Rank: 23/94	0.347 Rank: 34/94	0.773 Rank: 54/94	0.46389 (±0.00000) Rank: 16/94	251.11 Rank: 41/94	1916.44 Rank: 17/94	4.501 Rank: 27/94	0.428 Rank: 18/94	0.69407 (±0.00216) Rank: 18/94	0.57898 Rank: 14/94	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00013 SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1936.30	76.94 Rank: 87/94	0.323 Rank: 85/94	0.733 Rank: 73/94	0.30766 (±0.00075) Rank: 80/94	171.92 Rank: 66/94	1059.70 Rank: 89/94	3.687 Rank: 86/94	0.618 Rank: 85/94	0.42476 (±0.00103) Rank: 85/94	0.36621 Rank: 83/94	Challenge organizers (contact)	sift-def	sift (128 float32: 512 bytes)	SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-23	is_baseline
Submission ID: 00513 Personal Inplementation SIFT (GP... Size: 512 bytes. Matches: built-in	1900.60	70.04 Rank: 89/94	0.085 Rank: 94/94	0.002 Rank: 94/94	0.07782 (±0.00022) Rank: 90/94	173.06 Rank: 63/94	948.78 Rank: 90/94	3.816 Rank: 84/94	0.887 Rank: 92/94	0.09835 (±0.00068) Rank: 91/94	0.08809 Rank: 91/94	feyman_priv (contact)	sift-gpu	sift-gpu (128 float32: 512 bytes)	SIFT with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00609 Key.Net-s-ref + HyNet w/ DEGENSA... Size: 512 bytes. Matches: built-in	2032.93	270.04 Rank: 17/94	0.461 Rank: 2/94	0.820 Rank: 10/94	0.46902 (±0.00032) Rank: 12/94	280.40 Rank: 33/94	1489.62 Rank: 40/94	4.689 Rank: 15/94	0.436 Rank: 20/94	0.68120 (±0.00119) Rank: 20/94	0.57511 Rank: 17/94	Barroso-Laguna, Axel and Tian, Yurun, Ng, Tony and Mikolajczyk, Krystian (contact)	keynet-s-ref	hynet (128 float32: 512 bytes)		N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00033 SOSNet, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	119.91 Rank: 62/94	0.323 Rank: 75/94	0.769 Rank: 58/94	0.37632 (±0.00056) Rank: 63/94	145.47 Rank: 84/94	1271.41 Rank: 69/94	4.045 Rank: 74/94	0.524 Rank: 67/94	0.56072 (±0.00077) Rank: 69/94	0.46852 Rank: 69/94	Challenge organizers (contact)	sift-def	sosnet (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00701 GeoDesc (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	132.69 Rank: 58/94	0.333 Rank: 51/94	0.798 Rank: 32/94	0.41364 (±0.00033) Rank: 44/94	161.05 Rank: 73/94	1287.26 Rank: 66/94	4.239 Rank: 56/94	0.502 Rank: 57/94	0.58373 (±0.00402) Rank: 62/94	0.49868 Rank: 52/94	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-06-01	is_baseline
Submission ID: 00040 DELF-GLD (128D), DEGENSAC Size: 512 bytes. Matches: built-in	2036.82	100.78 Rank: 80/94	0.109 Rank: 90/94	0.151 Rank: 91/94	0.08367 (±0.00047) Rank: 88/94	430.68 Rank: 11/94	2162.72 Rank: 11/94	2.535 Rank: 90/94	0.880 Rank: 90/94	0.13247 (±0.00116) Rank: 89/94	0.10807 Rank: 89/94	Challenge organizers (contact)	delf-gld-2k-128d	delf-gld-2k-128d (128 float32: 512 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 128 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00564 SIFT2k_2000_HardNet64_train_all_... Size: 512 bytes. Matches: built-in	1999.83	156.93 Rank: 39/94	0.336 Rank: 45/94	0.814 Rank: 14/94	0.42959 (±0.00060) Rank: 35/94	244.13 Rank: 43/94	1646.76 Rank: 32/94	4.334 Rank: 39/94	0.459 Rank: 31/94	0.64407 (±0.00339) Rank: 32/94	0.53683 Rank: 34/94	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2000-hardnet64-train-all-l2-138000 (128 float32: 512 bytes)	SIFT with 2000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00616 Sift-Fusion_Avg-NM-Net_End-to-En... Size: 512 bytes. Matches: custom	1892.70	142.38 Rank: 50/94	0.333 Rank: 67/94	0.801 Rank: 28/94	0.44818 (±0.00000) Rank: 24/94	146.58 Rank: 82/94	1339.45 Rank: 58/94	4.406 Rank: 34/94	0.456 Rank: 28/94	0.65563 (±0.00209) Rank: 29/94	0.55190 Rank: 24/94	Chen Zhao (contact)	siftdef	fusion-avg (128 float32: 512 bytes)	SIFT and Fusion_Avg, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00003 HardNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	141.66 Rank: 51/94	0.333 Rank: 51/94	0.803 Rank: 27/94	0.44235 (±0.00127) Rank: 27/94	193.09 Rank: 55/94	1435.93 Rank: 47/94	4.302 Rank: 48/94	0.474 Rank: 44/94	0.62275 (±0.00423) Rank: 43/94	0.53255 Rank: 37/94	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00030 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	1892.72	111.69 Rank: 72/94	0.333 Rank: 47/94	0.781 Rank: 46/94	0.39547 (±0.00027) Rank: 52/94	201.15 Rank: 51/94	1347.75 Rank: 56/94	4.091 Rank: 68/94	0.522 Rank: 66/94	0.56078 (±0.00264) Rank: 68/94	0.47812 Rank: 59/94	Challenge organizers (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00027 DELF-GLD (32D), PyRANSAC Size: 128 bytes. Matches: built-in	2036.82	91.18 Rank: 84/94	0.109 Rank: 90/94	0.122 Rank: 92/94	0.06197 (±0.00032) Rank: 94/94	453.19 Rank: 7/94	1805.48 Rank: 21/94	2.425 Rank: 92/94	0.916 Rank: 93/94	0.09163 (±0.00140) Rank: 92/94	0.07680 Rank: 93/94	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00037 GeoDesc, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	115.17 Rank: 69/94	0.323 Rank: 81/94	0.742 Rank: 72/94	0.34024 (±0.00050) Rank: 73/94	120.68 Rank: 91/94	1098.70 Rank: 85/94	4.002 Rank: 79/94	0.551 Rank: 79/94	0.50594 (±0.00073) Rank: 82/94	0.42309 Rank: 79/94	Challenge organizers (contact)	sift-def	geodesc (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00586 KeyNet+HardNet+NM-Net_v2 Size: 512 bytes. Matches: custom	2042.89	160.25 Rank: 37/94	0.435 Rank: 18/94	0.821 Rank: 7/94	0.40264 (±0.00000) Rank: 47/94	165.01 Rank: 70/94	1134.69 Rank: 84/94	4.720 Rank: 14/94	0.448 Rank: 25/94	0.67488 (±0.00197) Rank: 22/94	0.53876 Rank: 30/94	Chen Zhao (contact)	keynet	hardnet (128 float32: 512 bytes)	KeyNet and HardNet, followed by NM-Net_v2 and DEGENSAC.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00710 DISK-32D (epi) Size: 128 bytes. Matches: built-in	2048.00	352.43 Rank: 10/94	0.459 Rank: 3/94	0.826 Rank: 5/94	0.45946 (±0.00023) Rank: 18/94	484.30 Rank: 4/94	2324.68 Rank: 5/94	5.589 Rank: 3/94	0.427 Rank: 16/94	0.69938 (±0.00192) Rank: 16/94	0.57942 Rank: 13/94	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with epipolar constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 5x5 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00642 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: custom	2048.00	140.36 Rank: 52/94	0.450 Rank: 4/94	0.571 Rank: 87/94	0.19708 (±0.00000) Rank: 86/94	146.55 Rank: 83/94	1279.68 Rank: 67/94	4.057 Rank: 70/94	0.542 Rank: 78/94	0.53330 (±0.00263) Rank: 75/94	0.36519 Rank: 84/94	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. Ordered-Aware(OA) Network, trained from scratch on sift-side-8k features (inlier threshold of network output = 1.0, post process method = None, with other settings set to default), is used to compute robust matches and OANet is used in place of robust model estimator.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-07	is_submission
Submission ID: 00134 CV-DoG-AffNet-HardNet-kornia-DEG... Size: 512 bytes. Matches: built-in	2047.84	152.12 Rank: 45/94	0.339 Rank: 40/94	0.791 Rank: 36/94	0.41972 (±0.00027) Rank: 41/94	284.06 Rank: 30/94	1788.70 Rank: 24/94	4.191 Rank: 63/94	0.511 Rank: 60/94	0.58536 (±0.00125) Rank: 59/94	0.50254 Rank: 50/94	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00017 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	2031.83	63.43 Rank: 91/94	0.366 Rank: 29/94	0.523 Rank: 89/94	0.08243 (±0.00058) Rank: 89/94	120.93 Rank: 90/94	280.38 Rank: 94/94	2.191 Rank: 94/94	0.863 Rank: 89/94	0.02318 (±0.00229) Rank: 94/94	0.05280 Rank: 94/94	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00523 SEKD Size: 512 bytes. Matches: built-in	2043.44	116.81 Rank: 65/94	0.386 Rank: 26/94	0.799 Rank: 31/94	0.42811 (±0.00096) Rank: 36/94	166.41 Rank: 69/94	1163.31 Rank: 81/94	4.427 Rank: 31/94	0.456 Rank: 29/94	0.64919 (±0.00171) Rank: 30/94	0.53865 Rank: 31/94	Yafei Song, Ling Cai, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	Our method named SEKD: Self-Evolving Keypoint Detection and Description, where the SEKD model is trained using COCO test images. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-29	is_submission, is_challenge_2020
Submission ID: 00651 sp_ae_sg_degensac Size: 512 bytes. Matches: custom	1873.96	288.87 Rank: 15/94	0.323 Rank: 72/94	0.693 Rank: 78/94	0.44506 (±0.00000) Rank: 26/94	295.52 Rank: 26/94	1994.42 Rank: 15/94	4.730 Rank: 12/94	0.395 Rank: 9/94	0.73681 (±0.00306) Rank: 9/94	0.59093 Rank: 11/94	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-05	is_submission
Submission ID: 00035 HardNet (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	181.81 Rank: 29/94	0.333 Rank: 51/94	0.781 Rank: 45/94	0.43816 (±0.00058) Rank: 30/94	193.09 Rank: 55/94	1435.93 Rank: 47/94	4.302 Rank: 48/94	0.473 Rank: 43/94	0.62275 (±0.00423) Rank: 43/94	0.53045 Rank: 39/94	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline

Phototourism: unlimited keypoints, small descriptors (128 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00042 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	7150.21	161.98 Rank: 2/2	0.514 Rank: 2/2	0.653 Rank: 2/2	0.16159 (±0.00090) Rank: 2/2	910.31 Rank: 1/2	1423.38 Rank: 2/2	2.722 Rank: 2/2	0.897 Rank: 2/2	0.08054 (±0.00242) Rank: 2/2	0.12106 Rank: 2/2	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00010 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	7857.11	246.74 Rank: 1/2	0.553 Rank: 1/2	0.735 Rank: 1/2	0.30717 (±0.00122) Rank: 1/2	479.55 Rank: 2/2	2778.68 Rank: 1/2	3.393 Rank: 1/2	0.737 Rank: 1/2	0.36048 (±0.00382) Rank: 1/2	0.33383 Rank: 1/2	Challenge organizers (contact)	akaze-lowth	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline

Phototourism: restricted keypoints, small descriptors (128 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00025 DELF-GLD (32D), DEGENSAC Size: 128 bytes. Matches: built-in	2036.82	96.56 Rank: 3/6	0.109 Rank: 5/6	0.119 Rank: 6/6	0.06573 (±0.00030) Rank: 5/6	453.19 Rank: 2/6	1805.48 Rank: 3/6	2.425 Rank: 4/6	0.920 Rank: 6/6	0.09163 (±0.00140) Rank: 4/6	0.07868 Rank: 4/6	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00711 DISK-32D (depth) Size: 128 bytes. Matches: built-in	2048.00	341.83 Rank: 2/6	0.448 Rank: 2/6	0.842 Rank: 1/6	0.47005 (±0.00037) Rank: 1/6	441.16 Rank: 4/6	2231.55 Rank: 2/6	5.524 Rank: 2/6	0.415 Rank: 1/6	0.70220 (±0.00208) Rank: 1/6	0.58613 Rank: 1/6	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with depth-based constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 7x7 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00026 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	1657.83	69.77 Rank: 5/6	0.355 Rank: 4/6	0.622 Rank: 3/6	0.17632 (±0.00087) Rank: 3/6	224.40 Rank: 5/6	795.54 Rank: 5/6	3.311 Rank: 3/6	0.819 Rank: 3/6	0.23020 (±0.00466) Rank: 3/6	0.20326 Rank: 3/6	Challenge organizers (contact)	akaze-def	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00027 DELF-GLD (32D), PyRANSAC Size: 128 bytes. Matches: built-in	2036.82	91.18 Rank: 4/6	0.109 Rank: 5/6	0.122 Rank: 5/6	0.06197 (±0.00032) Rank: 6/6	453.19 Rank: 2/6	1805.48 Rank: 3/6	2.425 Rank: 4/6	0.916 Rank: 5/6	0.09163 (±0.00140) Rank: 4/6	0.07680 Rank: 5/6	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00710 DISK-32D (epi) Size: 128 bytes. Matches: built-in	2048.00	352.43 Rank: 1/6	0.459 Rank: 1/6	0.826 Rank: 2/6	0.45946 (±0.00023) Rank: 2/6	484.30 Rank: 1/6	2324.68 Rank: 1/6	5.589 Rank: 1/6	0.427 Rank: 2/6	0.69938 (±0.00192) Rank: 2/6	0.57942 Rank: 2/6	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with epipolar constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 5x5 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00017 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	2031.83	63.43 Rank: 6/6	0.366 Rank: 3/6	0.523 Rank: 4/6	0.08243 (±0.00058) Rank: 4/6	120.93 Rank: 6/6	280.38 Rank: 6/6	2.191 Rank: 6/6	0.863 Rank: 4/6	0.02318 (±0.00229) Rank: 6/6	0.05280 Rank: 6/6	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline

Phototourism: unlimited keypoints, large descriptors (2048 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00081 LogPolar w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	591.21 Rank: 33/147	0.472 Rank: 103/147	0.832 Rank: 83/147	0.52385 (±0.00113) Rank: 57/147	415.77 Rank: 92/147	4054.60 Rank: 72/147	4.316 Rank: 84/147	0.432 Rank: 68/147	0.69284 (±0.00429) Rank: 70/147	0.60835 Rank: 63/147	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00121 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	398.65 Rank: 81/147	0.472 Rank: 88/147	0.863 Rank: 30/147	0.53852 (±0.00039) Rank: 44/147	356.56 Rank: 109/147	3550.63 Rank: 94/147	4.275 Rank: 89/147	0.439 Rank: 74/147	0.68875 (±0.00138) Rank: 76/147	0.61364 Rank: 52/147	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00142 CV-DoG-MKD-Concat-pyransac Size: 512 bytes. Matches: built-in	7860.77	208.02 Rank: 136/147	0.472 Rank: 82/147	0.854 Rank: 34/147	0.40612 (±0.00056) Rank: 118/147	348.03 Rank: 121/147	3507.39 Rank: 100/147	4.169 Rank: 112/147	0.471 Rank: 113/147	0.64763 (±0.00344) Rank: 108/147	0.52687 Rank: 117/147	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00562 HardNet64-train-all-raw64-balanc... Size: 512 bytes. Matches: built-in	7831.92	370.11 Rank: 91/147	0.486 Rank: 77/147	0.807 Rank: 114/147	0.45770 (±0.00060) Rank: 111/147	575.43 Rank: 46/147	4756.46 Rank: 34/147	4.563 Rank: 36/147	0.435 Rank: 72/147	0.69780 (±0.00191) Rank: 65/147	0.57775 Rank: 89/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-raw64-balance-l2-224000-raw64 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 224000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00602 retrained sosnet Size: 512 bytes. Matches: built-in	7861.11	340.97 Rank: 104/147	0.472 Rank: 103/147	0.840 Rank: 70/147	0.48990 (±0.00046) Rank: 88/147	623.55 Rank: 34/147	4812.36 Rank: 27/147	4.233 Rank: 101/147	0.458 Rank: 99/147	0.66502 (±0.00158) Rank: 95/147	0.57746 Rank: 90/147	sosnet (contact)	siftdef	sosnet (128 float32: 512 bytes)	default setting and retrained sosnet	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00576 Upright-Sift + X-Net-lib w/ DEGE... Size: 512 bytes. Matches: built-in	7830.16	465.31 Rank: 62/147	0.486 Rank: 44/147	0.844 Rank: 61/147	0.53156 (±0.00027) Rank: 50/147	720.29 Rank: 27/147	5398.84 Rank: 20/147	4.547 Rank: 41/147	0.408 Rank: 37/147	0.71966 (±0.00160) Rank: 41/147	0.62561 Rank: 46/147	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	sift	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00086 LogPolar-Upright w/ MAGSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	732.28 Rank: 15/147	0.486 Rank: 45/147	0.844 Rank: 63/147	0.54060 (±0.00047) Rank: 42/147	505.37 Rank: 70/147	4414.11 Rank: 54/147	4.518 Rank: 46/147	0.421 Rank: 52/147	0.71092 (±0.00251) Rank: 46/147	0.62576 Rank: 45/147	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00061 L2-Net-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	358.37 Rank: 96/147	0.486 Rank: 45/147	0.845 Rank: 56/147	0.52012 (±0.00106) Rank: 62/147	369.60 Rank: 107/147	3538.91 Rank: 97/147	4.407 Rank: 60/147	0.447 Rank: 89/147	0.68108 (±0.00177) Rank: 85/147	0.60060 Rank: 72/147	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00084 LogPolar-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	459.75 Rank: 63/147	0.486 Rank: 45/147	0.852 Rank: 42/147	0.52316 (±0.00034) Rank: 59/147	483.93 Rank: 72/147	4405.57 Rank: 57/147	4.542 Rank: 42/147	0.422 Rank: 53/147	0.70680 (±0.00247) Rank: 53/147	0.61498 Rank: 50/147	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00638 MKDNet-polar Size: 512 bytes. Matches: built-in	7862.74	337.43 Rank: 107/147	0.472 Rank: 91/147	0.847 Rank: 51/147	0.51376 (±0.00106) Rank: 70/147	552.91 Rank: 49/147	4786.70 Rank: 29/147	4.259 Rank: 96/147	0.434 Rank: 71/147	0.69907 (±0.00048) Rank: 63/147	0.60642 Rank: 69/147	(contact)	sift	mkdnet-polar (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-11	is_submission
Submission ID: 00140 CV-DoG-TFeat-kornia-MAGSAC Size: 512 bytes. Matches: built-in	7860.77	292.56 Rank: 119/147	0.472 Rank: 82/147	0.819 Rank: 106/147	0.46677 (±0.00109) Rank: 102/147	265.53 Rank: 139/147	2905.25 Rank: 127/147	4.038 Rank: 128/147	0.487 Rank: 120/147	0.62608 (±0.00138) Rank: 119/147	0.54643 Rank: 109/147	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	N/A	21-02-05	is_baseline
Submission ID: 00634 SIFT8k-HardNetPS-first-submit Size: 512 bytes. Matches: built-in	7830.09	502.15 Rank: 54/147	0.486 Rank: 31/147	0.841 Rank: 66/147	0.49113 (±0.00083) Rank: 85/147	651.33 Rank: 29/147	5100.71 Rank: 22/147	4.370 Rank: 73/147	0.423 Rank: 56/147	0.70782 (±0.00264) Rank: 51/147	0.59948 Rank: 73/147	Xudong Zhang, Yuhao Zhou, Huanhuan Fan (contact)	sift	hardnetps (128 float32: 512 bytes)	SIFT and hardnet with 8k features for fisrt subimission.	N/A	N/A	20-12-15	is_submission
Submission ID: 00601 SIFT8k-HardNet64 Size: 512 bytes. Matches: built-in	6589.88	381.80 Rank: 85/147	0.467 Rank: 124/147	0.852 Rank: 39/147	0.52493 (±0.00044) Rank: 55/147	420.36 Rank: 89/147	3586.82 Rank: 93/147	4.494 Rank: 51/147	0.429 Rank: 64/147	0.70580 (±0.00229) Rank: 55/147	0.61536 Rank: 49/147	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, hardnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-29	is_submission, is_challenge_2020
Submission ID: 00591 Guided-Hardnet-Epoch2 Size: 512 bytes. Matches: custom	7829.63	761.99 Rank: 13/147	0.486 Rank: 45/147	0.823 Rank: 102/147	0.61081 (±0.00000) Rank: 1/147	785.57 Rank: 23/147	6330.70 Rank: 7/147	4.680 Rank: 7/147	0.358 Rank: 2/147	0.78288 (±0.00094) Rank: 2/147	0.69684 Rank: 1/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-24	is_submission, is_challenge_2020
Submission ID: 00594 CV-DoG-HardNet8-PT Size: 512 bytes. Matches: built-in	7829.26	579.51 Rank: 37/147	0.484 Rank: 78/147	0.872 Rank: 15/147	0.58375 (±0.00046) Rank: 11/147	573.22 Rank: 47/147	4489.23 Rank: 49/147	4.619 Rank: 18/147	0.403 Rank: 31/147	0.72432 (±0.00357) Rank: 34/147	0.65403 Rank: 22/147	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift8k	h8e512pt (128 float32: 512 bytes)	HardNet8 with PCA compression	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00611 sift and hardnet64 train scale(1... Size: 512 bytes. Matches: built-in	7830.09	622.13 Rank: 22/147	0.486 Rank: 33/147	0.871 Rank: 17/147	0.58870 (±0.00041) Rank: 5/147	899.14 Rank: 14/147	6086.16 Rank: 12/147	4.647 Rank: 10/147	0.386 Rank: 15/147	0.75127 (±0.00234) Rank: 14/147	0.66999 Rank: 8/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-l2-val-14000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 14000), FLANN disabled	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00597 SIFT8k-HardNet64 Size: 512 bytes. Matches: built-in	6589.88	381.75 Rank: 86/147	0.467 Rank: 124/147	0.852 Rank: 40/147	0.52511 (±0.00056) Rank: 54/147	420.42 Rank: 88/147	3595.93 Rank: 92/147	4.496 Rank: 50/147	0.417 Rank: 45/147	0.70738 (±0.00218) Rank: 52/147	0.61625 Rank: 47/147	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, hardnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-27	is_submission, is_challenge_2020
Submission ID: 00636 MKDNet-cart Size: 512 bytes. Matches: built-in	7862.74	260.22 Rank: 127/147	0.472 Rank: 91/147	0.819 Rank: 107/147	0.46478 (±0.00044) Rank: 106/147	432.81 Rank: 85/147	4142.25 Rank: 70/147	4.050 Rank: 127/147	0.463 Rank: 104/147	0.66048 (±0.00254) Rank: 98/147	0.56263 Rank: 102/147	(contact)	sift	mkdnet-cart (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-13	is_submission
Submission ID: 00050 GeoDesc-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	410.23 Rank: 76/147	0.486 Rank: 45/147	0.827 Rank: 95/147	0.48259 (±0.00060) Rank: 94/147	394.67 Rank: 104/147	3863.42 Rank: 81/147	4.380 Rank: 67/147	0.443 Rank: 81/147	0.67682 (±0.00127) Rank: 90/147	0.57971 Rank: 87/147	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00034 D2-Net (single-scale), DEGENSAC Size: 2048 bytes. Matches: built-in	5665.31	251.55 Rank: 128/147	0.288 Rank: 142/147	0.360 Rank: 143/147	0.18499 (±0.00022) Rank: 136/147	740.01 Rank: 26/147	4584.07 Rank: 45/147	3.506 Rank: 138/147	0.696 Rank: 142/147	0.41726 (±0.00175) Rank: 141/147	0.30112 Rank: 137/147	Challenge organizers (contact)	d2net-singlescale	d2net-singlescale (512 float32: 2048 bytes)	D2-Net, single-scale model, up to 8000 features. Trained on the MegaDepth dataset, removing scenes which overlap with the Phototourism test set. Stereo with DEGENSAC and optimal parameters	http://openaccess.thecvf.com/content_CVPR_2019/papers/Dusmanu_D2-Net_A_Trainable_CNN_for_Joint_Description_and_Detection_of_CVPR_2019_paper.pdf	https://github.com/mihaidusmanu/d2-net	20-04-22	is_baseline
Submission ID: 00076 SOSNet w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	563.27 Rank: 41/147	0.472 Rank: 103/147	0.846 Rank: 54/147	0.55170 (±0.00042) Rank: 32/147	508.66 Rank: 67/147	4502.19 Rank: 47/147	4.377 Rank: 71/147	0.405 Rank: 35/147	0.71820 (±0.00298) Rank: 42/147	0.63495 Rank: 35/147	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00511 r2d2-wasfi Size: 512 bytes. Matches: built-in	7861.03	276.55 Rank: 122/147	0.503 Rank: 17/147	0.762 Rank: 123/147	0.37893 (±0.00023) Rank: 125/147	416.35 Rank: 91/147	2834.69 Rank: 130/147	4.293 Rank: 88/147	0.480 Rank: 117/147	0.64359 (±0.00103) Rank: 111/147	0.51126 Rank: 124/147	Chen Shen (contact)	r2d2	r2d2-wasfi-epoch25-pretrained-n16-8k (128 float32: 512 bytes)	r2d2 with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00067 GeoDesc w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	453.39 Rank: 65/147	0.472 Rank: 103/147	0.835 Rank: 79/147	0.50559 (±0.00081) Rank: 77/147	395.12 Rank: 102/147	3838.97 Rank: 83/147	4.264 Rank: 94/147	0.442 Rank: 79/147	0.68032 (±0.00099) Rank: 88/147	0.59295 Rank: 82/147	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00079 LogPolar w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	441.77 Rank: 68/147	0.472 Rank: 103/147	0.852 Rank: 38/147	0.53396 (±0.00086) Rank: 48/147	415.77 Rank: 92/147	4054.60 Rank: 72/147	4.316 Rank: 84/147	0.432 Rank: 70/147	0.69284 (±0.00429) Rank: 70/147	0.61340 Rank: 53/147	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00508 modified-r2d2 Size: 512 bytes. Matches: built-in	7791.82	160.64 Rank: 143/147	0.517 Rank: 15/147	0.785 Rank: 119/147	0.32815 (±0.00037) Rank: 127/147	328.39 Rank: 130/147	2811.27 Rank: 131/147	4.250 Rank: 98/147	0.488 Rank: 121/147	0.62899 (±0.00193) Rank: 115/147	0.47857 Rank: 127/147	caoliang (contact)	modified-r2d2	modified-r2d2 (128 float32: 512 bytes)	r2d2 with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	https://europe.naverlabs.com/wp-content/uploads/2019/09/R2D2-Repeatable-and-Reliable-Detector-and-Descriptor-2.pdf	https://github.com/naver/r2d2	20-04-25	is_submission, is_challenge_2020
Submission ID: 00514 UprightRootSIFT-AdaLAM Size: 512 bytes. Matches: custom	6449.42	619.87 Rank: 23/147	0.436 Rank: 132/147	0.758 Rank: 125/147	0.31372 (±0.00000) Rank: 129/147	640.08 Rank: 32/147	5286.27 Rank: 21/147	4.343 Rank: 76/147	0.398 Rank: 25/147	0.72130 (±0.00085) Rank: 40/147	0.51751 Rank: 119/147	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Using upright RootSIFT with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance.	https://arxiv.org/abs/2006.04250	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00052 HardNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	363.51 Rank: 93/147	0.472 Rank: 103/147	0.853 Rank: 37/147	0.52325 (±0.00098) Rank: 58/147	402.15 Rank: 95/147	3895.00 Rank: 79/147	4.345 Rank: 74/147	0.426 Rank: 61/147	0.69653 (±0.00108) Rank: 66/147	0.60989 Rank: 60/147	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00501 Guided matching with Upright roo... Size: 512 bytes. Matches: custom	7829.27	609.35 Rank: 27/147	0.486 Rank: 29/147	0.786 Rank: 118/147	0.49072 (±0.00000) Rank: 86/147	1363.98 Rank: 3/147	7835.75 Rank: 1/147	4.219 Rank: 103/147	0.454 Rank: 95/147	0.68064 (±0.00390) Rank: 87/147	0.58568 Rank: 85/147	(contact)	sift	upright-root-sift (128 float32: 512 bytes)	In submission	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00122 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	528.67 Rank: 45/147	0.472 Rank: 88/147	0.838 Rank: 72/147	0.53291 (±0.00047) Rank: 49/147	356.56 Rank: 109/147	3550.63 Rank: 94/147	4.275 Rank: 89/147	0.443 Rank: 82/147	0.68875 (±0.00138) Rank: 76/147	0.61083 Rank: 58/147	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00558 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	333.94 Rank: 109/147	0.284 Rank: 143/147	0.427 Rank: 140/147	0.01806 (±0.00000) Rank: 145/147	341.75 Rank: 127/147	2003.56 Rank: 138/147	3.735 Rank: 135/147	0.593 Rank: 134/147	0.51048 (±0.00436) Rank: 134/147	0.26427 Rank: 140/147	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 10) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00614 ContextDesc Upright + Mutual Che... Size: 512 bytes. Matches: custom	7830.09	647.63 Rank: 18/147	0.487 Rank: 25/147	0.846 Rank: 55/147	0.57826 (±0.00000) Rank: 17/147	668.00 Rank: 28/147	5612.57 Rank: 17/147	4.666 Rank: 8/147	0.367 Rank: 4/147	0.77041 (±0.00298) Rank: 5/147	0.67433 Rank: 5/147	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00625 HardNet64-train-all-l2-val-14000... Size: 512 bytes. Matches: built-in	7830.09	605.12 Rank: 29/147	0.486 Rank: 33/147	0.872 Rank: 16/147	0.58776 (±0.00048) Rank: 7/147	899.14 Rank: 14/147	6095.78 Rank: 11/147	4.645 Rank: 11/147	0.389 Rank: 19/147	0.74849 (±0.00117) Rank: 17/147	0.66812 Rank: 11/147	Ximin Zheng, Sheng He, Guanlin Liang (contact)	sift8k	hardnet64-train-all-l2-val-14000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 14000), FLANN disabled	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00540 ASLV2+OANetV2+DEGENSAC Size: 512 bytes. Matches: custom	5982.87	1044.40 Rank: 5/147	0.550 Rank: 11/147	0.758 Rank: 124/147	0.50173 (±0.00000) Rank: 80/147	1067.11 Rank: 6/147	4587.47 Rank: 44/147	4.948 Rank: 2/147	0.386 Rank: 16/147	0.75275 (±0.00186) Rank: 13/147	0.62724 Rank: 43/147	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	aslv2	aslv2 (128 float32: 512 bytes)	ASL detector and descriptor, 8k keypoints, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00049 GeoDesc-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	317.71 Rank: 114/147	0.486 Rank: 45/147	0.850 Rank: 46/147	0.48769 (±0.00064) Rank: 90/147	394.67 Rank: 104/147	3863.42 Rank: 81/147	4.380 Rank: 67/147	0.440 Rank: 76/147	0.67682 (±0.00127) Rank: 90/147	0.58225 Rank: 86/147	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00600 ContextDesc Upright + Mutual Che... Size: 512 bytes. Matches: custom	7830.09	624.55 Rank: 20/147	0.487 Rank: 25/147	0.847 Rank: 53/147	0.57344 (±0.00000) Rank: 19/147	644.39 Rank: 31/147	5427.23 Rank: 19/147	4.700 Rank: 4/147	0.368 Rank: 5/147	0.77043 (±0.00133) Rank: 4/147	0.67194 Rank: 6/147	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-29	is_submission, is_challenge_2020
Submission ID: 00532 affnet-hardnet Size: 512 bytes. Matches: built-in	7925.53	226.75 Rank: 133/147	0.452 Rank: 130/147	0.649 Rank: 133/147	0.31958 (±0.00055) Rank: 128/147	243.28 Rank: 142/147	3155.72 Rank: 119/147	3.595 Rank: 137/147	0.554 Rank: 133/147	0.52578 (±0.00343) Rank: 133/147	0.42268 Rank: 130/147	caoliang (contact)	affnet	hardnet (128 float32: 512 bytes)	affnet up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1711.06704	https://github.com/ducha-aiki/affnet	20-04-24	is_submission, is_challenge_2020
Submission ID: 00071 L2-Net w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	366.00 Rank: 92/147	0.472 Rank: 103/147	0.852 Rank: 44/147	0.52953 (±0.00043) Rank: 51/147	339.00 Rank: 128/147	3424.94 Rank: 105/147	4.206 Rank: 105/147	0.452 Rank: 93/147	0.66445 (±0.00213) Rank: 96/147	0.59699 Rank: 77/147	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00074 L2-Net w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	481.03 Rank: 57/147	0.472 Rank: 103/147	0.830 Rank: 91/147	0.52519 (±0.00039) Rank: 53/147	339.00 Rank: 128/147	3424.94 Rank: 105/147	4.206 Rank: 105/147	0.448 Rank: 90/147	0.66445 (±0.00213) Rank: 96/147	0.59482 Rank: 81/147	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-23	is_baseline
Submission ID: 00054 HardNet w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	477.64 Rank: 58/147	0.472 Rank: 103/147	0.830 Rank: 90/147	0.51809 (±0.00053) Rank: 64/147	402.15 Rank: 95/147	3895.00 Rank: 79/147	4.345 Rank: 74/147	0.423 Rank: 58/147	0.69653 (±0.00108) Rank: 66/147	0.60731 Rank: 67/147	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00073 L2-Net-Upright w/ MAGSAC (no FLA... Size: 512 bytes. Matches: built-in	7829.63	577.91 Rank: 38/147	0.486 Rank: 45/147	0.837 Rank: 78/147	0.53911 (±0.00062) Rank: 43/147	395.53 Rank: 100/147	3603.85 Rank: 88/147	4.382 Rank: 64/147	0.455 Rank: 96/147	0.68491 (±0.00338) Rank: 81/147	0.61201 Rank: 57/147	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00572 Guided HardNet qht Size: 512 bytes. Matches: custom	7829.63	885.09 Rank: 8/147	0.486 Rank: 45/147	0.774 Rank: 122/147	0.54663 (±0.00000) Rank: 35/147	538.28 Rank: 54/147	4687.01 Rank: 36/147	4.644 Rank: 13/147	0.376 Rank: 10/147	0.75721 (±0.00140) Rank: 11/147	0.65192 Rank: 25/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-qht (128 float32: 512 bytes)	sift and hardnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00621 CV-DoG-HardNet8-PTv2 Size: 512 bytes. Matches: built-in	7830.09	583.12 Rank: 35/147	0.486 Rank: 31/147	0.880 Rank: 4/147	0.58997 (±0.00054) Rank: 4/147	577.37 Rank: 44/147	4476.61 Rank: 50/147	4.638 Rank: 15/147	0.407 Rank: 36/147	0.73096 (±0.00227) Rank: 24/147	0.66046 Rank: 17/147	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift8k	h8e512pt (128 float32: 512 bytes)	HardNet8 with PCA compression, batch sampling from few images	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00138 CV-DoG-TFeat-kornia-DEGENSAC Size: 512 bytes. Matches: built-in	7860.77	234.75 Rank: 132/147	0.472 Rank: 82/147	0.831 Rank: 86/147	0.46486 (±0.00039) Rank: 105/147	265.53 Rank: 139/147	2905.25 Rank: 127/147	4.038 Rank: 128/147	0.487 Rank: 119/147	0.62608 (±0.00138) Rank: 119/147	0.54547 Rank: 111/147	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	N/A	21-02-05	is_baseline
Submission ID: 00065 SOSNet-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	407.29 Rank: 78/147	0.486 Rank: 45/147	0.864 Rank: 29/147	0.54098 (±0.00086) Rank: 41/147	521.37 Rank: 62/147	4473.48 Rank: 51/147	4.597 Rank: 23/147	0.417 Rank: 43/147	0.72179 (±0.00330) Rank: 37/147	0.63139 Rank: 38/147	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00595 SIFT8k_8000_HardNet64-train-all-... Size: 512 bytes. Matches: built-in	7830.09	600.42 Rank: 31/147	0.486 Rank: 33/147	0.867 Rank: 23/147	0.58265 (±0.00102) Rank: 14/147	786.40 Rank: 22/147	5595.19 Rank: 18/147	4.603 Rank: 22/147	0.390 Rank: 20/147	0.74372 (±0.00317) Rank: 23/147	0.66319 Rank: 16/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-ben86-l2-210000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 210000 and iou 0.86), FLANN disabled	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00566 Guided-HardNet2qhtp.json Size: 512 bytes. Matches: custom	7829.63	517.72 Rank: 49/147	0.486 Rank: 45/147	0.874 Rank: 11/147	0.58251 (±0.00000) Rank: 15/147	448.12 Rank: 81/147	4072.82 Rank: 71/147	4.607 Rank: 20/147	0.387 Rank: 17/147	0.74640 (±0.00246) Rank: 20/147	0.66445 Rank: 14/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnetnd32qhtp (128 float32: 512 bytes)	sift and hardnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00080 LogPolar w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	376.42 Rank: 88/147	0.472 Rank: 103/147	0.837 Rank: 76/147	0.50188 (±0.00044) Rank: 79/147	399.70 Rank: 97/147	4036.37 Rank: 74/147	4.340 Rank: 77/147	0.429 Rank: 65/147	0.69100 (±0.00154) Rank: 73/147	0.59644 Rank: 78/147	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00019 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	7829.24	355.69 Rank: 97/147	0.487 Rank: 23/147	0.844 Rank: 64/147	0.51009 (±0.00033) Rank: 74/147	548.18 Rank: 51/147	4406.41 Rank: 56/147	4.385 Rank: 62/147	0.442 Rank: 78/147	0.68859 (±0.00254) Rank: 79/147	0.59934 Rank: 74/147	Challenge organizers (contact)	sift-lowth	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00062 L2-Net-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	471.61 Rank: 60/147	0.486 Rank: 45/147	0.822 Rank: 103/147	0.51450 (±0.00058) Rank: 68/147	369.60 Rank: 107/147	3538.91 Rank: 97/147	4.407 Rank: 60/147	0.448 Rank: 91/147	0.68108 (±0.00177) Rank: 85/147	0.59779 Rank: 76/147	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00628 Multiple kernel local descriptor Size: 512 bytes. Matches: built-in	7862.74	331.20 Rank: 110/147	0.472 Rank: 91/147	0.829 Rank: 92/147	0.48380 (±0.00048) Rank: 93/147	541.02 Rank: 53/147	4639.37 Rank: 41/147	4.144 Rank: 116/147	0.463 Rank: 105/147	0.66704 (±0.00645) Rank: 93/147	0.57542 Rank: 92/147	(contact)	sift	mkdpcawt (128 float32: 512 bytes)	based on the paper [Understanding and Improving Kernel Local Descriptors]	https://arxiv.org/pdf/1811.11147	https://arxiv.org/abs/1811.11147	20-10-28	is_submission
Submission ID: 00522 SIFT and HardNet64 train scale(5... Size: 512 bytes. Matches: built-in	7830.09	513.57 Rank: 51/147	0.486 Rank: 33/147	0.852 Rank: 45/147	0.55385 (±0.00076) Rank: 30/147	822.07 Rank: 18/147	5660.19 Rank: 16/147	4.555 Rank: 37/147	0.404 Rank: 33/147	0.72744 (±0.00455) Rank: 28/147	0.64065 Rank: 31/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-raw64 (128 float32: 512 bytes)	SIFT with 8000 keypoints(raw 64), hardnet64 with 128 descriptors, FLANN disabled	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00530 SIFT-patch Size: 1024 bytes. Matches: built-in	7861.62	83.83 Rank: 146/147	0.472 Rank: 99/147	0.577 Rank: 136/147	0.22977 (±0.00029) Rank: 133/147	80.10 Rank: 146/147	973.09 Rank: 146/147	2.007 Rank: 146/147	0.726 Rank: 143/147	0.08184 (±0.00372) Rank: 146/147	0.15581 Rank: 145/147	feyman_priv (contact)	sift8k	l2net-ith-rcface (256 float32: 1024 bytes)	sift8k with l2net trained on google-landmark-dataset-v1(in 1000 class)	N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00132 CV-DoG-AffNet-HardNet-kornia-MAG... Size: 512 bytes. Matches: built-in	7833.97	516.85 Rank: 50/147	0.486 Rank: 74/147	0.870 Rank: 19/147	0.54116 (±0.00028) Rank: 40/147	580.47 Rank: 40/147	4671.35 Rank: 38/147	4.565 Rank: 33/147	0.403 Rank: 27/147	0.72668 (±0.00115) Rank: 30/147	0.63392 Rank: 36/147	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00057 HardNet-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	582.52 Rank: 36/147	0.486 Rank: 45/147	0.841 Rank: 67/147	0.53730 (±0.00082) Rank: 46/147	480.13 Rank: 74/147	4167.34 Rank: 65/147	4.566 Rank: 31/147	0.416 Rank: 42/147	0.71542 (±0.00274) Rank: 44/147	0.62636 Rank: 44/147	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00048 GeoDesc w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	362.25 Rank: 94/147	0.472 Rank: 103/147	0.816 Rank: 110/147	0.46887 (±0.00025) Rank: 99/147	350.43 Rank: 119/147	3601.67 Rank: 90/147	4.234 Rank: 99/147	0.447 Rank: 88/147	0.65553 (±0.00444) Rank: 102/147	0.56220 Rank: 103/147	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00120 CV-DoG-HardNetAmos-8k Size: 512 bytes. Matches: built-in	7860.98	265.75 Rank: 125/147	0.472 Rank: 88/147	0.873 Rank: 13/147	0.46073 (±0.00062) Rank: 110/147	356.56 Rank: 109/147	3550.63 Rank: 94/147	4.275 Rank: 89/147	0.439 Rank: 75/147	0.68875 (±0.00138) Rank: 76/147	0.57474 Rank: 93/147	Challenge organizers (contact)	sift8k	hardnetamos (128 float32: 512 bytes)	CV-DoG-HardNetAmos with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00502 Guided matching with Upright roo... Size: 512 bytes. Matches: custom	7829.27	491.96 Rank: 56/147	0.486 Rank: 29/147	0.828 Rank: 94/147	0.50310 (±0.01761) Rank: 78/147	809.76 Rank: 20/147	5966.91 Rank: 13/147	4.462 Rank: 54/147	0.425 Rank: 60/147	0.71009 (±0.00414) Rank: 48/147	0.60659 Rank: 68/147	(contact)	sift	upright-root-sift (128 float32: 512 bytes)	In submission	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00510 sift and hardnet64 Size: 512 bytes. Matches: built-in	7861.62	326.87 Rank: 111/147	0.472 Rank: 101/147	0.845 Rank: 59/147	0.51149 (±0.00018) Rank: 72/147	521.58 Rank: 61/147	4587.88 Rank: 43/147	4.222 Rank: 102/147	0.424 Rank: 59/147	0.69458 (±0.00340) Rank: 68/147	0.60304 Rank: 70/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-v2 (128 float32: 512 bytes)	SIFT with 8000 keypoints, hardnet64 with 128 descriptors	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00106 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	326.20 Rank: 112/147	0.490 Rank: 20/147	0.809 Rank: 113/147	0.46326 (±0.00089) Rank: 107/147	324.62 Rank: 131/147	3030.67 Rank: 123/147	4.173 Rank: 109/147	0.462 Rank: 103/147	0.62829 (±0.00112) Rank: 116/147	0.54578 Rank: 110/147	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00618 Sift-Sem + HyNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7530.17	476.84 Rank: 59/147	0.490 Rank: 19/147	0.882 Rank: 3/147	0.57281 (±0.00047) Rank: 21/147	809.82 Rank: 19/147	5868.33 Rank: 14/147	4.585 Rank: 26/147	0.390 Rank: 22/147	0.74557 (±0.00393) Rank: 21/147	0.65919 Rank: 19/147	Barroso-Laguna, Axel and Tian, Yurun, Ng, Tony and Mikolajczyk, Krystian (contact)	sift-semantics	hynet (128 float32: 512 bytes)		N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00068 GeoDesc-Upright w/ DEGENSAC (no ... Size: 512 bytes. Matches: built-in	7829.63	409.87 Rank: 77/147	0.486 Rank: 45/147	0.869 Rank: 20/147	0.52674 (±0.00049) Rank: 52/147	458.60 Rank: 77/147	4146.83 Rank: 68/147	4.412 Rank: 58/147	0.431 Rank: 67/147	0.70442 (±0.00189) Rank: 57/147	0.61558 Rank: 48/147	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00070 GeoDesc-Upright w/ MAGSAC (no FL... Size: 512 bytes. Matches: built-in	7829.63	534.05 Rank: 44/147	0.486 Rank: 45/147	0.848 Rank: 49/147	0.52078 (±0.00028) Rank: 61/147	458.60 Rank: 77/147	4146.83 Rank: 68/147	4.412 Rank: 58/147	0.432 Rank: 69/147	0.70442 (±0.00189) Rank: 57/147	0.61260 Rank: 56/147	Challenge organizers (contact)	sift8k	geodesc-upright (128 float32: 512 bytes)	Upright GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00537 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	1476.71 Rank: 1/147	0.284 Rank: 143/147	0.145 Rank: 147/147	0.00178 (±0.00000) Rank: 147/147	1506.82 Rank: 2/147	2538.44 Rank: 134/147	3.388 Rank: 142/147	0.669 Rank: 140/147	0.42682 (±0.00259) Rank: 140/147	0.21430 Rank: 143/147	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished]	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00536 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	1164.84 Rank: 3/147	0.368 Rank: 136/147	0.341 Rank: 144/147	0.00607 (±0.00000) Rank: 146/147	1191.31 Rank: 4/147	2215.65 Rank: 136/147	4.014 Rank: 132/147	0.524 Rank: 132/147	0.58741 (±0.00250) Rank: 128/147	0.29674 Rank: 138/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished]	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00075 SOSNet w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	424.61 Rank: 74/147	0.472 Rank: 103/147	0.868 Rank: 22/147	0.55867 (±0.00060) Rank: 26/147	508.66 Rank: 67/147	4502.19 Rank: 47/147	4.377 Rank: 71/147	0.404 Rank: 32/147	0.71820 (±0.00298) Rank: 42/147	0.63844 Rank: 32/147	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-23	is_baseline
Submission ID: 00059 L2-Net w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	297.34 Rank: 118/147	0.472 Rank: 103/147	0.835 Rank: 81/147	0.49523 (±0.00042) Rank: 83/147	314.54 Rank: 134/147	3314.19 Rank: 114/147	4.204 Rank: 107/147	0.467 Rank: 110/147	0.65665 (±0.00287) Rank: 99/147	0.57594 Rank: 91/147	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00042 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	7150.21	161.98 Rank: 141/147	0.514 Rank: 16/147	0.653 Rank: 131/147	0.16159 (±0.00090) Rank: 137/147	910.31 Rank: 13/147	1423.38 Rank: 144/147	2.722 Rank: 145/147	0.897 Rank: 147/147	0.08054 (±0.00242) Rank: 147/147	0.12106 Rank: 147/147	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00520 SIFT and HardNet64 train scale(5... Size: 512 bytes. Matches: built-in	7830.09	400.19 Rank: 80/147	0.486 Rank: 33/147	0.837 Rank: 77/147	0.50589 (±0.00059) Rank: 76/147	619.25 Rank: 35/147	4959.78 Rank: 25/147	4.384 Rank: 63/147	0.456 Rank: 97/147	0.69172 (±0.00048) Rank: 72/147	0.59880 Rank: 75/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-scale5 (128 float32: 512 bytes)	SIFT with 8000 keypoints(size scaled by 5), hardnet64 with 128 descriptors, FLANN disabled	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00055 HardNet-Upright w/ DEGENSAC (no ... Size: 512 bytes. Matches: built-in	7829.63	527.59 Rank: 47/147	0.486 Rank: 45/147	0.876 Rank: 7/147	0.57279 (±0.00120) Rank: 22/147	509.07 Rank: 65/147	4250.40 Rank: 60/147	4.548 Rank: 39/147	0.413 Rank: 40/147	0.72309 (±0.00141) Rank: 35/147	0.64794 Rank: 27/147	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00613 HardNet64-data-aug-sort-51 Size: 512 bytes. Matches: built-in	7830.09	624.09 Rank: 21/147	0.486 Rank: 33/147	0.870 Rank: 18/147	0.58727 (±0.00076) Rank: 8/147	964.80 Rank: 9/147	6350.68 Rank: 5/147	4.644 Rank: 14/147	0.383 Rank: 13/147	0.74952 (±0.00162) Rank: 16/147	0.66839 Rank: 10/147	Ximin Zheng, Sheng He, Guanlin Liang (contact)	sift8k	hardnet64-data-aug-sort-51 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 124000 and data augument), FLANN disabled	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00554 ContextDesc-Upright w/ DEGENSAC ... Size: 512 bytes. Matches: built-in	7830.09	470.69 Rank: 61/147	0.487 Rank: 25/147	0.877 Rank: 5/147	0.55697 (±0.00079) Rank: 28/147	543.45 Rank: 52/147	4543.76 Rank: 46/147	4.498 Rank: 49/147	0.405 Rank: 34/147	0.72902 (±0.00255) Rank: 27/147	0.64299 Rank: 30/147	Zixin Luo, Jiahui Zhang, Hongkai Chen (contact)	sift-def	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value and use only single orientation to increase performance. FLANN disabled.	https://arxiv.org/abs/1904.04084	https://github.com/lzx551402/contextdesc	20-05-11	is_submission, is_challenge_2020
Submission ID: 00571 ContextDesc Upright + OANetV2 + ... Size: 512 bytes. Matches: custom	7830.09	827.45 Rank: 9/147	0.487 Rank: 25/147	0.775 Rank: 120/147	0.53810 (±0.00000) Rank: 45/147	855.38 Rank: 16/147	6561.52 Rank: 3/147	4.522 Rank: 44/147	0.370 Rank: 8/147	0.76702 (±0.00081) Rank: 6/147	0.65256 Rank: 23/147	Jiahui Zhang, Zixin Luo, Hongkai Chen (contact)	contextdesc-upright	contextdesc-upright (128 float32: 512 bytes)	ContextDesc with 8000 SIFT features, using improved OANet matcher and DEGENSAC post-processing	N/A	N/A	20-05-21	is_submission, is_challenge_2020
Submission ID: 00078 SOSNet-Upright w/ MAGSAC (no FLA... Size: 512 bytes. Matches: built-in	7829.63	679.94 Rank: 17/147	0.486 Rank: 45/147	0.855 Rank: 33/147	0.56626 (±0.00020) Rank: 23/147	600.23 Rank: 37/147	4765.43 Rank: 32/147	4.584 Rank: 27/147	0.403 Rank: 28/147	0.73027 (±0.00284) Rank: 25/147	0.64826 Rank: 26/147	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00630 Superpoint_modified Size: 512 bytes. Matches: built-in	7927.15	25.26 Rank: 147/147	0.410 Rank: 135/147	0.328 Rank: 145/147	0.06569 (±0.00036) Rank: 140/147	70.01 Rank: 147/147	895.58 Rank: 147/147	2.963 Rank: 144/147	0.849 Rank: 146/147	0.18293 (±0.00230) Rank: 144/147	0.12431 Rank: 146/147	Anonymous (to be released: 2020-6-12)	superpoint-modified	superpoint-modified (128 float32: 512 bytes)	Modified superpoint output.	N/A	N/A	20-11-27	is_submission
Submission ID: 00063 SOSNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	340.09 Rank: 105/147	0.472 Rank: 103/147	0.854 Rank: 35/147	0.51891 (±0.00067) Rank: 63/147	440.21 Rank: 82/147	4178.82 Rank: 63/147	4.378 Rank: 69/147	0.419 Rank: 48/147	0.70078 (±0.00375) Rank: 60/147	0.60984 Rank: 61/147	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00548 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	343.78 Rank: 102/147	0.368 Rank: 136/147	0.607 Rank: 135/147	0.28795 (±0.00000) Rank: 131/147	351.53 Rank: 115/147	1699.08 Rank: 141/147	4.124 Rank: 120/147	0.507 Rank: 126/147	0.61370 (±0.00246) Rank: 122/147	0.45082 Rank: 129/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00521 SIFT8k-gift Size: 512 bytes. Matches: built-in	6589.48	426.77 Rank: 73/147	0.467 Rank: 127/147	0.830 Rank: 87/147	0.48128 (±0.00056) Rank: 95/147	452.18 Rank: 79/147	3770.17 Rank: 87/147	4.595 Rank: 25/147	0.444 Rank: 84/147	0.67443 (±0.00253) Rank: 92/147	0.57785 Rank: 88/147	Chen Shen (contact)	sift-def	gift (128 float32: 512 bytes)	sift-nodups, gift with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00504 ASLFeat (MS) w/ DEGENSAC (no FLA... Size: 512 bytes. Matches: built-in	6948.83	390.79 Rank: 82/147	0.550 Rank: 10/147	0.797 Rank: 116/147	0.46102 (±0.00067) Rank: 109/147	510.80 Rank: 64/147	3817.14 Rank: 85/147	4.577 Rank: 29/147	0.444 Rank: 83/147	0.68246 (±0.00173) Rank: 84/147	0.57174 Rank: 97/147	Zixin Luo, Jiahui Zhang (contact)	aslfeat	aslfeat (128 float32: 512 bytes)	ASLFeat (MS) with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC. FLANN disabled.	https://arxiv.org/abs/2003.10071	https://github.com/lzx551402/ASLFeat	20-04-25	is_submission, is_challenge_2020
Submission ID: 00010 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	7857.11	246.74 Rank: 130/147	0.553 Rank: 9/147	0.735 Rank: 127/147	0.30717 (±0.00122) Rank: 130/147	479.55 Rank: 76/147	2778.68 Rank: 132/147	3.393 Rank: 141/147	0.737 Rank: 145/147	0.36048 (±0.00382) Rank: 143/147	0.33383 Rank: 132/147	Challenge organizers (contact)	akaze-lowth	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00141 CV-DoG-MKD-Concat-DEGENSAC Size: 512 bytes. Matches: built-in	7860.77	305.75 Rank: 116/147	0.472 Rank: 82/147	0.845 Rank: 58/147	0.48465 (±0.00122) Rank: 92/147	348.03 Rank: 121/147	3507.39 Rank: 100/147	4.169 Rank: 112/147	0.467 Rank: 109/147	0.64763 (±0.00344) Rank: 108/147	0.56614 Rank: 99/147	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00051 HardNet w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	432.32 Rank: 72/147	0.472 Rank: 103/147	0.866 Rank: 25/147	0.55430 (±0.00031) Rank: 29/147	426.84 Rank: 86/147	4001.40 Rank: 77/147	4.339 Rank: 79/147	0.419 Rank: 47/147	0.70962 (±0.00191) Rank: 49/147	0.63196 Rank: 37/147	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00639 MKDNet-hardnet Size: 512 bytes. Matches: built-in	7862.74	341.96 Rank: 103/147	0.472 Rank: 91/147	0.847 Rank: 52/147	0.51643 (±0.00099) Rank: 65/147	558.95 Rank: 48/147	4811.45 Rank: 28/147	4.268 Rank: 92/147	0.420 Rank: 51/147	0.69845 (±0.00564) Rank: 64/147	0.60744 Rank: 65/147	(contact)	sift	mkdnet-hardnet (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-11	is_submission
Submission ID: 00005 SURF (OpenCV), DEGENSAC Size: 256 bytes. Matches: built-in	7728.57	125.94 Rank: 144/147	0.432 Rank: 134/147	0.684 Rank: 130/147	0.24948 (±0.00065) Rank: 132/147	928.29 Rank: 12/147	3455.28 Rank: 104/147	3.428 Rank: 139/147	0.733 Rank: 144/147	0.40490 (±0.00448) Rank: 142/147	0.32719 Rank: 133/147	Challenge organizers (contact)	surf-lowth	surf (64 float32: 256 bytes)	SURF with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00069 GeoDesc w/ DEGENSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	348.52 Rank: 101/147	0.472 Rank: 103/147	0.856 Rank: 32/147	0.51112 (±0.00070) Rank: 73/147	395.12 Rank: 102/147	3838.97 Rank: 83/147	4.264 Rank: 94/147	0.443 Rank: 80/147	0.68032 (±0.00099) Rank: 88/147	0.59572 Rank: 80/147	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00559 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	164.43 Rank: 140/147	0.284 Rank: 143/147	0.650 Rank: 132/147	0.05206 (±0.00000) Rank: 142/147	167.59 Rank: 144/147	1549.91 Rank: 142/147	3.989 Rank: 134/147	0.622 Rank: 136/147	0.47700 (±0.00167) Rank: 136/147	0.26453 Rank: 139/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 5) [Mishkin 2019]	N/A	N/A	20-05-10	is_submission, is_challenge_2020
Submission ID: 00006 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7829.24	319.29 Rank: 113/147	0.487 Rank: 23/147	0.830 Rank: 89/147	0.48742 (±0.00042) Rank: 91/147	525.57 Rank: 58/147	4147.20 Rank: 67/147	4.258 Rank: 97/147	0.460 Rank: 100/147	0.65664 (±0.00231) Rank: 101/147	0.57203 Rank: 96/147	Challenge organizers (contact)	sift-lowth	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00573 Guided-HardNet-qht Size: 512 bytes. Matches: custom	7829.63	887.83 Rank: 7/147	0.486 Rank: 45/147	0.774 Rank: 121/147	0.55224 (±0.00000) Rank: 31/147	538.28 Rank: 54/147	4693.85 Rank: 35/147	4.650 Rank: 9/147	0.381 Rank: 11/147	0.75930 (±0.00177) Rank: 8/147	0.65577 Rank: 20/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-qht (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00066 SOSNet-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	537.85 Rank: 43/147	0.486 Rank: 45/147	0.841 Rank: 68/147	0.53531 (±0.00061) Rank: 47/147	521.37 Rank: 62/147	4473.48 Rank: 51/147	4.597 Rank: 23/147	0.414 Rank: 41/147	0.72179 (±0.00330) Rank: 37/147	0.62855 Rank: 42/147	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00114 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	348.95 Rank: 100/147	0.547 Rank: 12/147	0.800 Rank: 115/147	0.43347 (±0.00084) Rank: 115/147	347.39 Rank: 124/147	3209.10 Rank: 116/147	4.126 Rank: 117/147	0.517 Rank: 128/147	0.58657 (±0.00120) Rank: 129/147	0.51002 Rank: 125/147	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00624 Guided-HardNet-OANet Size: 512 bytes. Matches: custom	7829.63	765.34 Rank: 12/147	0.486 Rank: 45/147	0.820 Rank: 104/147	0.60261 (±0.00000) Rank: 2/147	788.49 Rank: 21/147	6346.62 Rank: 6/147	4.682 Rank: 6/147	0.355 Rank: 1/147	0.78550 (±0.00157) Rank: 1/147	0.69405 Rank: 2/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 8k features, first using the oanet trained from scratch then guided-matching, setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00015 Root-SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7860.73	274.85 Rank: 123/147	0.472 Rank: 97/147	0.845 Rank: 57/147	0.48887 (±0.00011) Rank: 89/147	437.74 Rank: 84/147	3814.80 Rank: 86/147	4.151 Rank: 115/147	0.458 Rank: 98/147	0.65067 (±0.00155) Rank: 104/147	0.56977 Rank: 98/147	Challenge organizers (contact)	sift-lowth	rootsift (128 float32: 512 bytes)	Root-SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00102 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	815.40 Rank: 10/147	0.582 Rank: 2/147	0.788 Rank: 117/147	0.47395 (±0.00073) Rank: 97/147	356.21 Rank: 112/147	3366.01 Rank: 108/147	4.319 Rank: 81/147	0.464 Rank: 106/147	0.64829 (±0.00174) Rank: 105/147	0.56112 Rank: 104/147	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00519 SOSNet-Upright-AdaLAM Size: 512 bytes. Matches: custom	7830.09	803.50 Rank: 11/147	0.486 Rank: 33/147	0.818 Rank: 108/147	0.56056 (±0.00000) Rank: 25/147	827.85 Rank: 17/147	6157.62 Rank: 10/147	4.700 Rank: 5/147	0.381 Rank: 12/147	0.75917 (±0.00446) Rank: 9/147	0.65987 Rank: 18/147	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	Using upright SOSNet descriptors with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00024 SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	7860.73	238.66 Rank: 131/147	0.472 Rank: 97/147	0.824 Rank: 99/147	0.45426 (±0.00097) Rank: 112/147	418.86 Rank: 90/147	3515.63 Rank: 99/147	4.001 Rank: 133/147	0.502 Rank: 125/147	0.60193 (±0.00185) Rank: 126/147	0.52810 Rank: 116/147	Challenge organizers (contact)	sift-lowth	sift (128 float32: 512 bytes)	SIFT with (up to) 8000 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00056 HardNet-Upright w/ DEGENSAC Size: 512 bytes. Matches: built-in	7829.63	439.36 Rank: 69/147	0.486 Rank: 45/147	0.864 Rank: 28/147	0.54393 (±0.00027) Rank: 39/147	480.13 Rank: 74/147	4167.34 Rank: 65/147	4.566 Rank: 31/147	0.417 Rank: 44/147	0.71542 (±0.00274) Rank: 44/147	0.62967 Rank: 41/147	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00045 D2-Net (multi-scale), DEGENSAC Size: 2048 bytes. Matches: built-in	6924.14	338.82 Rank: 106/147	0.321 Rank: 141/147	0.386 Rank: 141/147	0.20486 (±0.00062) Rank: 135/147	740.77 Rank: 25/147	5696.95 Rank: 15/147	3.326 Rank: 143/147	0.660 Rank: 139/147	0.42782 (±0.00544) Rank: 139/147	0.31634 Rank: 134/147	Challenge organizers (contact)	d2net-multiscale	d2net-multiscale (512 float32: 2048 bytes)	D2-Net, multi-scale model, up to 8000 features. Trained on the MegaDepth dataset, removing scenes which overlap with the Phototourism test set. Stereo with DEGENSAC and optimal parameters	http://openaccess.thecvf.com/content_CVPR_2019/papers/Dusmanu_D2-Net_A_Trainable_CNN_for_Joint_Description_and_Detection_of_CVPR_2019_paper.pdf	https://github.com/mihaidusmanu/d2-net	20-04-22	is_baseline
Submission ID: 00047 GeoDesc w/ DEGENSAC Size: 512 bytes. Matches: built-in	7861.11	281.36 Rank: 121/147	0.472 Rank: 103/147	0.838 Rank: 75/147	0.47259 (±0.00033) Rank: 98/147	350.43 Rank: 119/147	3601.67 Rank: 90/147	4.234 Rank: 99/147	0.447 Rank: 87/147	0.65553 (±0.00444) Rank: 102/147	0.56406 Rank: 101/147	Challenge organizers (contact)	sift8k	geodesc (128 float32: 512 bytes)	GeoDesc descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00072 L2-Net-Upright w/ DEGENSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	435.68 Rank: 70/147	0.486 Rank: 45/147	0.859 Rank: 31/147	0.54497 (±0.00003) Rank: 36/147	395.53 Rank: 100/147	3603.85 Rank: 88/147	4.382 Rank: 64/147	0.452 Rank: 94/147	0.68491 (±0.00338) Rank: 81/147	0.61494 Rank: 51/147	Challenge organizers (contact)	sift8k	l2net-upright (128 float32: 512 bytes)	Upright L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00518 UprightRootSIFT-AdaLAM Size: 512 bytes. Matches: custom	6449.42	435.53 Rank: 71/147	0.436 Rank: 132/147	0.825 Rank: 98/147	0.49625 (±0.00000) Rank: 82/147	449.49 Rank: 80/147	4353.56 Rank: 59/147	4.453 Rank: 56/147	0.397 Rank: 24/147	0.72490 (±0.00190) Rank: 33/147	0.61057 Rank: 59/147	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Using upright RootSIFT with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00526 ASLFeat-MS Size: 512 bytes. Matches: built-in	7384.19	456.55 Rank: 64/147	0.578 Rank: 5/147	0.752 Rank: 126/147	0.42208 (±0.00054) Rank: 116/147	532.83 Rank: 57/147	4002.80 Rank: 76/147	4.521 Rank: 45/147	0.447 Rank: 86/147	0.66568 (±0.00347) Rank: 94/147	0.54388 Rank: 113/147	Zixin Luo, Jiahui Zhang (contact)	aslfeat-ms	aslfeat-ms (128 float32: 512 bytes)	ASLFeat (joint predition of detectors and descriptors with multi-scale input) with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: 00627 Multiple kernel local descriptor Size: 512 bytes. Matches: built-in	7862.74	354.57 Rank: 98/147	0.472 Rank: 91/147	0.845 Rank: 60/147	0.50919 (±0.00037) Rank: 75/147	580.16 Rank: 43/147	4784.46 Rank: 30/147	4.210 Rank: 104/147	0.436 Rank: 73/147	0.68323 (±0.00354) Rank: 83/147	0.59621 Rank: 79/147	(contact)	sift	mkdlw (128 float32: 512 bytes)	based on the paper [Understanding and Improving Kernel Local Descriptors]	https://arxiv.org/pdf/1811.11147	https://arxiv.org/abs/1811.11147	20-10-28	is_submission
Submission ID: 00143 CV-DoG-MKD-Concat-magsac Size: 512 bytes. Matches: built-in	7860.77	381.37 Rank: 87/147	0.472 Rank: 82/147	0.833 Rank: 82/147	0.48096 (±0.00051) Rank: 96/147	348.03 Rank: 121/147	3507.39 Rank: 100/147	4.169 Rank: 112/147	0.471 Rank: 112/147	0.64763 (±0.00344) Rank: 108/147	0.56429 Rank: 100/147	Challenge organizers (contact)	sift	mkd-concat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the MKD-Concat descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1811.11147	N/A	21-02-05	is_baseline
Submission ID: 00083 LogPolar-Upright w/ DEGENSAC (no... Size: 512 bytes. Matches: built-in	7829.63	543.18 Rank: 42/147	0.486 Rank: 45/147	0.865 Rank: 27/147	0.55102 (±0.00015) Rank: 33/147	505.37 Rank: 70/147	4414.11 Rank: 54/147	4.518 Rank: 46/147	0.422 Rank: 54/147	0.71092 (±0.00251) Rank: 46/147	0.63097 Rank: 39/147	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00053 HardNet w/ MAGSAC (no FLANN) Size: 512 bytes. Matches: built-in	7861.11	575.07 Rank: 39/147	0.472 Rank: 103/147	0.842 Rank: 65/147	0.55022 (±0.00001) Rank: 34/147	426.84 Rank: 86/147	4001.40 Rank: 77/147	4.339 Rank: 79/147	0.419 Rank: 50/147	0.70962 (±0.00191) Rank: 49/147	0.62992 Rank: 40/147	Challenge organizers (contact)	sift8k	hardnet (128 float32: 512 bytes)	HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00557 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	614.16 Rank: 24/147	0.368 Rank: 136/147	0.513 Rank: 138/147	0.05482 (±0.00000) Rank: 141/147	626.57 Rank: 33/147	1793.00 Rank: 140/147	4.314 Rank: 86/147	0.517 Rank: 129/147	0.56933 (±0.00268) Rank: 132/147	0.31207 Rank: 135/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 10) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00113 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	290.24 Rank: 120/147	0.547 Rank: 12/147	0.814 Rank: 111/147	0.44501 (±0.00077) Rank: 114/147	347.39 Rank: 124/147	3209.10 Rank: 116/147	4.126 Rank: 117/147	0.519 Rank: 131/147	0.58657 (±0.00120) Rank: 129/147	0.51579 Rank: 120/147	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00130 CV-DoG-AffNet-HardNet-kornia-DEG... Size: 512 bytes. Matches: built-in	7833.97	403.41 Rank: 79/147	0.486 Rank: 74/147	0.883 Rank: 2/147	0.54468 (±0.00090) Rank: 37/147	580.47 Rank: 40/147	4671.35 Rank: 38/147	4.565 Rank: 33/147	0.402 Rank: 26/147	0.72668 (±0.00115) Rank: 30/147	0.63568 Rank: 34/147	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00533 upright-sift8k-hardnet Size: 512 bytes. Matches: built-in	6589.88	371.42 Rank: 90/147	0.467 Rank: 124/147	0.852 Rank: 43/147	0.52463 (±0.00070) Rank: 56/147	404.02 Rank: 94/147	3501.62 Rank: 103/147	4.483 Rank: 52/147	0.428 Rank: 63/147	0.70123 (±0.00081) Rank: 59/147	0.61293 Rank: 54/147	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-24	is_submission, is_challenge_2020
Submission ID: 00541 sift and sosnet64 train scale(12... Size: 512 bytes. Matches: built-in	7830.09	603.56 Rank: 30/147	0.486 Rank: 33/147	0.866 Rank: 24/147	0.58268 (±0.00061) Rank: 13/147	957.95 Rank: 10/147	6312.38 Rank: 8/147	4.607 Rank: 21/147	0.385 Rank: 14/147	0.74476 (±0.00137) Rank: 22/147	0.66372 Rank: 15/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-sos (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 348000), FLANN disabled	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00563 HardNet64-train-all-SOS-812000 Size: 512 bytes. Matches: built-in	7830.09	612.51 Rank: 25/147	0.486 Rank: 33/147	0.869 Rank: 21/147	0.58664 (±0.00095) Rank: 9/147	972.47 Rank: 8/147	6369.03 Rank: 4/147	4.612 Rank: 19/147	0.388 Rank: 18/147	0.74668 (±0.00251) Rank: 18/147	0.66666 Rank: 13/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00105 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	261.60 Rank: 126/147	0.490 Rank: 20/147	0.826 Rank: 97/147	0.46555 (±0.00055) Rank: 104/147	324.62 Rank: 131/147	3030.67 Rank: 123/147	4.173 Rank: 109/147	0.461 Rank: 101/147	0.62829 (±0.00112) Rank: 116/147	0.54692 Rank: 108/147	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00110 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	317.13 Rank: 115/147	0.482 Rank: 79/147	0.820 Rank: 105/147	0.46657 (±0.00079) Rank: 103/147	311.54 Rank: 136/147	3061.54 Rank: 120/147	4.105 Rank: 121/147	0.475 Rank: 115/147	0.62964 (±0.00428) Rank: 112/147	0.54810 Rank: 107/147	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00598 HarrisZ (more kpt) + DeepOrienta... Size: 512 bytes. Matches: custom	4478.94	563.62 Rank: 40/147	0.455 Rank: 128/147	0.711 Rank: 128/147	0.39767 (±0.00000) Rank: 123/147	576.83 Rank: 45/147	2707.54 Rank: 133/147	4.381 Rank: 66/147	0.445 Rank: 85/147	0.69090 (±0.00163) Rank: 75/147	0.54428 Rank: 112/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ (start scale 2) [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.5, degeneracy check true, confidence 0.98, max iter 500000) [Mishkin 2019]	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00082 LogPolar w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	496.36 Rank: 55/147	0.472 Rank: 103/147	0.817 Rank: 109/147	0.49129 (±0.00011) Rank: 84/147	399.70 Rank: 97/147	4036.37 Rank: 74/147	4.340 Rank: 77/147	0.429 Rank: 66/147	0.69100 (±0.00154) Rank: 73/147	0.59115 Rank: 83/147	Challenge organizers (contact)	sift8k	logpolar96-fixed (128 float32: 512 bytes)	LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00610 Hardnet-Upright-AdaLAM Size: 512 bytes. Matches: custom	6556.61	627.71 Rank: 19/147	0.442 Rank: 131/147	0.828 Rank: 93/147	0.58300 (±0.00000) Rank: 12/147	645.47 Rank: 30/147	5074.91 Rank: 23/147	4.575 Rank: 30/147	0.361 Rank: 3/147	0.77056 (±0.00064) Rank: 3/147	0.67678 Rank: 4/147	Luca Cavalli, Viktor Larsson, Martin Oswald, Torsten Sattler, Marc Pollefeys (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	Using upright Hardnet descriptors with 8000 features, nearest neighbor matching and outlier rejection enforcing local affine consistency within a confidence-based adaptive error tolerance. Matches post-processed with DEGENSAC.	https://arxiv.org/abs/2006.04250	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00568 Guided-SOSNet-lib-p Size: 512 bytes. Matches: custom	7829.63	508.45 Rank: 52/147	0.486 Rank: 45/147	0.874 Rank: 12/147	0.57982 (±0.00000) Rank: 16/147	524.66 Rank: 59/147	4618.86 Rank: 42/147	4.632 Rank: 16/147	0.369 Rank: 6/147	0.75888 (±0.00437) Rank: 10/147	0.66935 Rank: 9/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Zhongkun Chen, Jingchao Zhou, Pengfei Xu (contact)	sift8k	sosnet-lib-p (128 float32: 512 bytes)	sift and sosnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00117 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	299.02 Rank: 117/147	0.577 Rank: 6/147	0.831 Rank: 84/147	0.46793 (±0.00035) Rank: 101/147	350.69 Rank: 116/147	3327.71 Rank: 111/147	4.076 Rank: 124/147	0.487 Rank: 118/147	0.60691 (±0.00338) Rank: 123/147	0.53742 Rank: 114/147	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00139 CV-DoG-TFeat-kornia-PyRANSAC Size: 512 bytes. Matches: built-in	7860.77	160.77 Rank: 142/147	0.472 Rank: 82/147	0.839 Rank: 71/147	0.40079 (±0.00116) Rank: 119/147	265.53 Rank: 139/147	2905.25 Rank: 127/147	4.038 Rank: 128/147	0.488 Rank: 122/147	0.62608 (±0.00138) Rank: 119/147	0.51344 Rank: 123/147	Challenge organizers (contact)	sift	tfeat (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by the TFeat descriptor. Implementation: OpenCV + kornia library	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf	21-02-05	is_baseline
Submission ID: 00567 Guided-HardNet32-v1-lib-qht-p Size: 512 bytes. Matches: custom	7829.63	520.40 Rank: 48/147	0.486 Rank: 45/147	0.875 Rank: 10/147	0.58509 (±0.00000) Rank: 10/147	536.93 Rank: 56/147	4685.16 Rank: 37/147	4.645 Rank: 12/147	0.376 Rank: 9/147	0.75702 (±0.00159) Rank: 12/147	0.67105 Rank: 7/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhiwei Ruan, Zhongkun Chen, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet32-v1-lib-qht-p (128 float32: 512 bytes)	sift and sosnet with 8k features, using the guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-15	is_submission, is_challenge_2020
Submission ID: 00077 SOSNet-Upright w/ DEGENSAC (no F... Size: 512 bytes. Matches: built-in	7829.63	508.38 Rank: 53/147	0.486 Rank: 45/147	0.877 Rank: 6/147	0.57385 (±0.00041) Rank: 18/147	600.23 Rank: 37/147	4765.43 Rank: 32/147	4.584 Rank: 27/147	0.403 Rank: 30/147	0.73027 (±0.00284) Rank: 25/147	0.65206 Rank: 24/147	Challenge organizers (contact)	sift8k	sosnet-upright (128 float32: 512 bytes)	Upright SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-23	is_baseline
Submission ID: 00101 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	598.28 Rank: 32/147	0.582 Rank: 2/147	0.826 Rank: 96/147	0.49856 (±0.00038) Rank: 81/147	356.21 Rank: 112/147	3366.01 Rank: 108/147	4.319 Rank: 81/147	0.464 Rank: 107/147	0.64829 (±0.00174) Rank: 105/147	0.57342 Rank: 95/147	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00556 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	385.16 Rank: 84/147	0.368 Rank: 136/147	0.646 Rank: 134/147	0.04192 (±0.00000) Rank: 144/147	392.52 Rank: 106/147	1475.19 Rank: 143/147	4.454 Rank: 55/147	0.601 Rank: 135/147	0.48498 (±0.00118) Rank: 135/147	0.26345 Rank: 141/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 5) [Mishkin 2019]	N/A	N/A	20-05-11	is_submission, is_challenge_2020
Submission ID: 00524 SIFT-patch Size: 512 bytes. Matches: built-in	7861.62	84.08 Rank: 145/147	0.472 Rank: 99/147	0.574 Rank: 137/147	0.22805 (±0.00076) Rank: 134/147	81.93 Rank: 145/147	991.42 Rank: 145/147	1.934 Rank: 147/147	0.687 Rank: 141/147	0.08662 (±0.00384) Rank: 145/147	0.15733 Rank: 144/147	feyman_priv (contact)	sift8k	l2net-arcface (128 float32: 512 bytes)	sift8k with l2net trained on google-landmark-dataset-v1(in 1000 class)	N/A	N/A	20-04-26	is_submission, is_challenge_2020
Submission ID: 00546 HarrisZ + DeepOrientation + SOSN... Size: 512 bytes. Matches: custom	2410.18	1020.86 Rank: 6/147	0.368 Rank: 136/147	0.366 Rank: 142/147	0.07780 (±0.00000) Rank: 139/147	1047.01 Rank: 7/147	2197.09 Rank: 137/147	4.023 Rank: 131/147	0.509 Rank: 127/147	0.59222 (±0.00276) Rank: 127/147	0.33501 Rank: 131/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 15, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-02	is_baseline
Submission ID: 00635 MKDNet-indep Size: 512 bytes. Matches: built-in	7862.74	336.51 Rank: 108/147	0.472 Rank: 91/147	0.848 Rank: 50/147	0.51519 (±0.00069) Rank: 67/147	550.86 Rank: 50/147	4772.22 Rank: 31/147	4.265 Rank: 93/147	0.427 Rank: 62/147	0.70004 (±0.00374) Rank: 62/147	0.60762 Rank: 64/147	(contact)	sift	mkdnet-indep (128 float32: 512 bytes)	based on the paper [Explicit spatial encoding for deep local descriptors], trained on Liberty set from PhotoTourism dataset	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	https://openaccess.thecvf.com/content_CVPR_2019/papers/Mukundan_Explicit_Spatial_Encoding_for_Deep_Local_Descriptors_CVPR_2019_paper.pdf	20-12-13	is_submission
Submission ID: 00709 DISK (LCC/depth) Size: 512 bytes. Matches: built-in	7844.17	1238.52 Rank: 2/147	0.644 Rank: 1/147	0.852 Rank: 41/147	0.55847 (±0.00084) Rank: 27/147	1663.81 Rank: 1/147	7483.98 Rank: 2/147	5.922 Rank: 1/147	0.391 Rank: 23/147	0.75024 (±0.00316) Rank: 15/147	0.65435 Rank: 21/147	Under review! NeurIPS anonymous submission, ID 1194 (To be released: 21-06-02)	disk-cc-continued-20-imsize-1024-nms-3-nump-8000	disk-cc-continued-20-imsize-1024-nms-3-nump-8000 (128 float32: 512 bytes)	Local feature model learned via policy gradient. Model trained with a cycle-consistency loss and supervised with depth. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 3x3 window. We take the top 8000 features by score.	N/A	N/A	20-06-02	is_submission, is_challenge_2020, is_under_review
Submission ID: 00535 hardnetlib32P-Upright w/ DEGENSA... Size: 512 bytes. Matches: built-in	7829.63	527.64 Rank: 46/147	0.486 Rank: 45/147	0.876 Rank: 8/147	0.57327 (±0.00078) Rank: 20/147	506.73 Rank: 69/147	4230.98 Rank: 62/147	4.517 Rank: 48/147	0.418 Rank: 46/147	0.72171 (±0.00156) Rank: 39/147	0.64749 Rank: 28/147	Chen Shen, Zhipeng Wang, Jun Zhang, Jingchao Zhou, Pengfei Xu (contact)	sift8k-no-dups-mps	hardnetlib32P (128 float32: 512 bytes)	sift and hardnet with 8k features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: 00108 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	171.63 Rank: 139/147	0.482 Rank: 79/147	0.848 Rank: 48/147	0.39839 (±0.00122) Rank: 122/147	311.54 Rank: 136/147	3061.54 Rank: 120/147	4.105 Rank: 121/147	0.475 Rank: 116/147	0.62964 (±0.00428) Rank: 112/147	0.51401 Rank: 122/147	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00104 VL-DoG-SIFT-8k Size: 512 bytes. Matches: built-in	7880.59	179.68 Rank: 138/147	0.490 Rank: 20/147	0.835 Rank: 80/147	0.39987 (±0.00068) Rank: 120/147	324.62 Rank: 131/147	3030.67 Rank: 123/147	4.173 Rank: 109/147	0.467 Rank: 111/147	0.62829 (±0.00112) Rank: 116/147	0.51408 Rank: 121/147	Challenge organizers (contact)	dog	vlsift (128 float32: 512 bytes)	VL-DoG-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00064 SOSNet w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	444.55 Rank: 67/147	0.472 Rank: 103/147	0.831 Rank: 85/147	0.51402 (±0.00057) Rank: 69/147	440.21 Rank: 82/147	4178.82 Rank: 63/147	4.378 Rank: 69/147	0.422 Rank: 55/147	0.70078 (±0.00375) Rank: 60/147	0.60740 Rank: 66/147	Challenge organizers (contact)	sift8k	sosnet (128 float32: 512 bytes)	SOSNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00590 Guided-HardNet-epoch4 Size: 512 bytes. Matches: custom	7829.63	586.24 Rank: 34/147	0.486 Rank: 45/147	0.875 Rank: 9/147	0.59919 (±0.00000) Rank: 3/147	604.81 Rank: 36/147	5062.27 Rank: 24/147	4.710 Rank: 3/147	0.369 Rank: 7/147	0.76219 (±0.00253) Rank: 7/147	0.68069 Rank: 3/147	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift8k	hardnet-epoch4 (128 float32: 512 bytes)	sift and hardnet with 8k features, using the modified guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-24	is_submission, is_challenge_2020
Submission ID: 00506 sift and hardnet64 Size: 512 bytes. Matches: built-in	7861.62	358.55 Rank: 95/147	0.472 Rank: 101/147	0.849 Rank: 47/147	0.52085 (±0.00009) Rank: 60/147	585.33 Rank: 39/147	4957.86 Rank: 26/147	4.294 Rank: 87/147	0.419 Rank: 49/147	0.70472 (±0.00257) Rank: 56/147	0.61279 Rank: 55/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64 (128 float32: 512 bytes)	SIFT with 8000 keypoints, hardnet64 with 128 descriptors	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00116 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	209.26 Rank: 135/147	0.577 Rank: 6/147	0.841 Rank: 69/147	0.39330 (±0.00052) Rank: 124/147	350.69 Rank: 116/147	3327.71 Rank: 111/147	4.076 Rank: 124/147	0.490 Rank: 124/147	0.60691 (±0.00338) Rank: 123/147	0.50011 Rank: 126/147	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00085 LogPolar-Upright w/ MAGSAC Size: 512 bytes. Matches: built-in	7829.63	611.19 Rank: 26/147	0.486 Rank: 45/147	0.830 Rank: 88/147	0.51287 (±0.00038) Rank: 71/147	483.93 Rank: 72/147	4405.57 Rank: 57/147	4.542 Rank: 42/147	0.423 Rank: 57/147	0.70680 (±0.00247) Rank: 53/147	0.60983 Rank: 62/147	Challenge organizers (contact)	sift8k	logpolar96-fixed-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-23	is_baseline
Submission ID: 00109 VL-DoGAff-SIFT-8k Size: 512 bytes. Matches: built-in	7892.05	250.11 Rank: 129/147	0.482 Rank: 79/147	0.838 Rank: 74/147	0.46795 (±0.00075) Rank: 100/147	311.54 Rank: 136/147	3061.54 Rank: 120/147	4.105 Rank: 121/147	0.475 Rank: 114/147	0.62964 (±0.00428) Rank: 112/147	0.54880 Rank: 106/147	Challenge organizers (contact)	dogaffine	vlsift (128 float32: 512 bytes)	VL-DoGAff-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00060 L2-Net w/ MAGSAC Size: 512 bytes. Matches: built-in	7861.11	386.81 Rank: 83/147	0.472 Rank: 103/147	0.813 Rank: 112/147	0.49054 (±0.00043) Rank: 87/147	314.54 Rank: 134/147	3314.19 Rank: 114/147	4.204 Rank: 107/147	0.465 Rank: 108/147	0.65665 (±0.00287) Rank: 99/147	0.57360 Rank: 94/147	Challenge organizers (contact)	sift8k	l2net (128 float32: 512 bytes)	L2-Net descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo.	http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_L2-Net_Deep_Learning_CVPR_2017_paper.pdf	https://github.com/yuruntian/L2-Net	20-04-22	is_baseline
Submission ID: 00542 sift and hardnet64 train scale(1... Size: 512 bytes. Matches: built-in	7830.09	607.44 Rank: 28/147	0.486 Rank: 33/147	0.872 Rank: 14/147	0.58801 (±0.00037) Rank: 6/147	950.74 Rank: 11/147	6286.36 Rank: 9/147	4.626 Rank: 17/147	0.390 Rank: 21/147	0.74662 (±0.00087) Rank: 19/147	0.66732 Rank: 12/147	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet64-train-all-l2 (128 float32: 512 bytes)	SIFT with 8000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled	N/A	N/A	20-05-01	is_submission, is_challenge_2020
Submission ID: 00569 Upright-SIFT-HardNet Size: 512 bytes. Matches: built-in	6667.86	376.15 Rank: 89/147	0.491 Rank: 18/147	0.844 Rank: 62/147	0.51560 (±0.00043) Rank: 66/147	397.09 Rank: 99/147	3417.56 Rank: 107/147	4.479 Rank: 53/147	0.448 Rank: 92/147	0.68803 (±0.00532) Rank: 80/147	0.60181 Rank: 71/147	caoliang (contact)	sift8k	hardnet (128 float32: 512 bytes)	SIFT up to 8000 keypoints, harnet extract descriptors.Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-05-20	is_submission, is_challenge_2020
Submission ID: 00118 VL-HessAffNet-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	350.03 Rank: 99/147	0.577 Rank: 6/147	0.824 Rank: 100/147	0.46263 (±0.00079) Rank: 108/147	350.69 Rank: 116/147	3327.71 Rank: 111/147	4.076 Rank: 124/147	0.489 Rank: 123/147	0.60691 (±0.00338) Rank: 123/147	0.53477 Rank: 115/147	Challenge organizers (contact)	hessian	affnetvlsift (128 float32: 512 bytes)	VL-HessAffNet-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with MAGSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00058 HardNet-Upright w/ MAGSAC (no FL... Size: 512 bytes. Matches: built-in	7829.63	707.86 Rank: 16/147	0.486 Rank: 45/147	0.853 Rank: 36/147	0.56374 (±0.00069) Rank: 24/147	509.07 Rank: 65/147	4250.40 Rank: 60/147	4.548 Rank: 39/147	0.412 Rank: 39/147	0.72309 (±0.00141) Rank: 35/147	0.64341 Rank: 29/147	Challenge organizers (contact)	sift8k	hardnet-upright (128 float32: 512 bytes)	Upright HardNet descriptors on DoG features (OpenCV), extracted with a low detection threshold to generate up to 8000 points. Using the built-in matcher: bidirectional filter with the both strategy. MAGSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00131 CV-DoG-AffNet-HardNet-kornia-PyR... Size: 512 bytes. Matches: built-in	7833.97	267.89 Rank: 124/147	0.486 Rank: 74/147	0.890 Rank: 1/147	0.45050 (±0.00103) Rank: 113/147	580.47 Rank: 40/147	4671.35 Rank: 38/147	4.565 Rank: 33/147	0.403 Rank: 29/147	0.72668 (±0.00115) Rank: 30/147	0.58859 Rank: 84/147	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00549 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	216.23 Rank: 134/147	0.284 Rank: 143/147	0.456 Rank: 139/147	0.14544 (±0.00000) Rank: 138/147	221.40 Rank: 143/147	1839.87 Rank: 139/147	3.608 Rank: 136/147	0.641 Rank: 137/147	0.47073 (±0.00158) Rank: 137/147	0.30808 Rank: 136/147	Fabio Bellavia (contact)	sift	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00607 HarrisZ (more kpt) + DeepOrienta... Size: 512 bytes. Matches: custom	4478.94	746.02 Rank: 14/147	0.455 Rank: 128/147	0.699 Rank: 129/147	0.40725 (±0.00000) Rank: 117/147	763.44 Rank: 24/147	2953.35 Rank: 126/147	4.414 Rank: 57/147	0.442 Rank: 77/147	0.69399 (±0.00179) Rank: 69/147	0.55062 Rank: 105/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	HarrisZ (start scale 2) [Bellavia et al. 2011] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 0.75, degeneracy check true, confidence 0.98, max iter 500000) [Mishkin 2019]	N/A	N/A	20-06-03	is_submission, is_challenge_2020
Submission ID: 00112 VL-Hess-SIFT-8k Size: 512 bytes. Matches: built-in	8000.00	204.37 Rank: 137/147	0.547 Rank: 12/147	0.823 Rank: 101/147	0.36954 (±0.00081) Rank: 126/147	347.39 Rank: 124/147	3209.10 Rank: 116/147	4.126 Rank: 117/147	0.517 Rank: 130/147	0.58657 (±0.00120) Rank: 129/147	0.47806 Rank: 128/147	Challenge organizers (contact)	hessian	vlsift (128 float32: 512 bytes)	VL-Hess-SIFT with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline
Submission ID: 00545 structured feature description Size: 512 bytes. Matches: built-in	7830.21	411.68 Rank: 75/147	0.486 Rank: 43/147	0.865 Rank: 26/147	0.54442 (±0.00084) Rank: 38/147	524.05 Rank: 60/147	4467.23 Rank: 53/147	4.551 Rank: 38/147	0.412 Rank: 38/147	0.72729 (±0.00300) Rank: 29/147	0.63586 Rank: 33/147	Mahdi Abolfazli Esfahani, Han Wang (contact)	sift	sfd (128 float32: 512 bytes)	structured descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC	N/A	N/A	20-04-29	is_submission, is_challenge_2020
Submission ID: 00547 SIFT + DeepOrientation + SOSNet ... Size: 512 bytes. Matches: custom	2826.82	1078.85 Rank: 4/147	0.284 Rank: 143/147	0.166 Rank: 146/147	0.04936 (±0.00000) Rank: 143/147	1106.68 Rank: 5/147	2494.00 Rank: 135/147	3.400 Rank: 140/147	0.646 Rank: 138/147	0.43054 (±0.00185) Rank: 138/147	0.23995 Rank: 142/147	Fabio Bellavia (contact)	hz	deep-oriented-sosnet (128 float32: 512 bytes)	SIFT (VLFeat implementation) [Lowe 2004] + Deep patch orientation [Yi et al. 2015] + SOSNet [Tian at al. 2019] (weights: sosnet-32x32-hpatches_a.pth) + Blob Matching [unpublished] + Delaunay Triangulation Matching (DTM) [unpublished] + PyRANSAC (threshold 15, degeneracy check true) [Mishkin 2019]	N/A	N/A	20-05-03	is_submission, is_challenge_2020
Submission ID: 00100 KeyNet-HardNet-8k Size: 512 bytes. Matches: built-in	7997.63	448.11 Rank: 66/147	0.582 Rank: 2/147	0.838 Rank: 73/147	0.39971 (±0.00147) Rank: 121/147	356.21 Rank: 112/147	3366.01 Rank: 108/147	4.319 Rank: 81/147	0.462 Rank: 102/147	0.64829 (±0.00174) Rank: 105/147	0.52400 Rank: 118/147	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 8000 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-04	is_baseline

Phototourism: restricted keypoints, large descriptors (2048 bytes)

Note: entries with the same multi-view configuration may seem duplicated. This is normal: performance is averaged across tasks.

		Stereo				Multiview					Avg.
Method	NF	NI	Rep. (3 pix.)	MS (3 pix.)	mAA (at 10^o)	NM	NL	TL	ATE	mAA (at 10⁰)	mAA (at 10⁰)	Authors	Keypoint	Descriptor	Summary	Paper	Website	Processing date	Flags
Submission ID: 00708 DISK (LCC/depth) Size: 512 bytes. Matches: built-in	2048.00	404.19 Rank: 7/108	0.448 Rank: 11/108	0.852 Rank: 1/108	0.51315 (±0.00028) Rank: 11/108	527.48 Rank: 2/108	2428.04 Rank: 3/108	5.545 Rank: 5/108	0.410 Rank: 15/108	0.72705 (±0.00094) Rank: 15/108	0.62010 Rank: 13/108	Michal Tyszkiewicz (contact)	disk-cc-continued-20-imsize-1024-nms-3-nump-2048	disk-cc-continued-20-imsize-1024-nms-3-nump-2048 (128 float32: 512 bytes)	Local feature model learned via policy gradient. Model trained with a cycle-consistency loss and supervised with depth. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 3x3 window. We take the top 2048 features by score.	N/A	N/A	20-06-03	is_submission, is_challenge_2020
Submission ID: 00608 SIFT2k_2048_HardNet64-train-all-... Size: 512 bytes. Matches: custom	2047.00	245.40 Rank: 27/108	0.346 Rank: 46/108	0.823 Rank: 6/108	0.49219 (±0.00000) Rank: 14/108	253.37 Rank: 49/108	1984.40 Rank: 22/108	4.607 Rank: 22/108	0.421 Rank: 19/108	0.70020 (±0.00213) Rank: 20/108	0.59619 Rank: 14/108	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	hardnet64-train-all-l2-138000-matched (128 float32: 512 bytes)	SIFT with 2048 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled, custom matches	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00702 L2-Net (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	117.11 Rank: 72/108	0.333 Rank: 60/108	0.808 Rank: 21/108	0.41918 (±0.00059) Rank: 46/108	179.79 Rank: 70/108	1361.34 Rank: 64/108	4.232 Rank: 65/108	0.481 Rank: 52/108	0.59682 (±0.00079) Rank: 61/108	0.50800 Rank: 52/108	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-06-01	is_baseline
Submission ID: 00663 disk_degree(patch)_End-to-End Size: 512 bytes. Matches: custom	2048.00	448.22 Rank: 3/108	0.447 Rank: 15/108	0.834 Rank: 4/108	0.53645 (±0.00000) Rank: 9/108	458.32 Rank: 5/108	2377.71 Rank: 4/108	5.657 Rank: 2/108	0.373 Rank: 8/108	0.75520 (±0.00336) Rank: 8/108	0.64582 Rank: 9/108	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree(patch)_End-to-End and DEGENSAC.	N/A	N/A	21-04-28	is_submission
Submission ID: 00653 sp_ae_sg_degensac_th Size: 512 bytes. Matches: custom	2048.00	293.72 Rank: 18/108	0.352 Rank: 42/108	0.772 Rank: 59/108	0.54380 (±0.00000) Rank: 6/108	300.66 Rank: 31/108	1801.43 Rank: 29/108	4.723 Rank: 18/108	0.366 Rank: 4/108	0.75988 (±0.00277) Rank: 7/108	0.65184 Rank: 7/108	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-14	is_submission
Submission ID: 00503 Guided matching hardnet Size: 512 bytes. Matches: custom	1892.71	149.75 Rank: 52/108	0.333 Rank: 74/108	0.795 Rank: 35/108	0.42159 (±0.00000) Rank: 44/108	254.13 Rank: 47/108	1754.99 Rank: 35/108	4.283 Rank: 61/108	0.464 Rank: 40/108	0.64144 (±0.00207) Rank: 40/108	0.53152 Rank: 43/108	(contact)	sift	upright-hardnet (128 float32: 512 bytes)	Sift keypoint ; upright hardnet descriptors ; custom matching: use a deep learning based coarse matcher as a first step. In a second step match the keypoint according to descriptor distances but only for the matches that are close to the coarse match prediction	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00703 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	1892.72	112.27 Rank: 80/108	0.333 Rank: 56/108	0.782 Rank: 45/108	0.39860 (±0.00077) Rank: 56/108	199.34 Rank: 61/108	1341.66 Rank: 66/108	4.090 Rank: 78/108	0.518 Rank: 70/108	0.56230 (±0.00234) Rank: 73/108	0.48045 Rank: 62/108	Challenge organizers (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-06-01	is_baseline
Submission ID: 00644 Example: Upright SIFT (OpenCV) Size: 512 bytes. Matches: built-in	1892.71	125.97 Rank: 67/108	0.333 Rank: 74/108	0.820 Rank: 11/108	0.43853 (±0.00030) Rank: 33/108	193.06 Rank: 66/108	1436.91 Rank: 55/108	4.278 Rank: 63/108	0.463 Rank: 38/108	0.63822 (±0.00469) Rank: 41/108	0.53838 Rank: 37/108	(contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	SIFT with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	https://opencv.org	21-03-13	is_submission
Submission ID: 00028 DELF-GLD (512D), DEGENSAC Size: 2048 bytes. Matches: built-in	2036.82	98.87 Rank: 92/108	0.109 Rank: 102/108	0.170 Rank: 103/108	0.09156 (±0.00045) Rank: 99/108	437.64 Rank: 11/108	2296.72 Rank: 6/108	2.567 Rank: 102/108	0.877 Rank: 102/108	0.14991 (±0.00513) Rank: 101/108	0.12073 Rank: 101/108	Challenge organizers (contact)	delf-gld-2k-512d	delf-gld-2k-512d (512 float32: 2048 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 512 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-23	is_baseline
Submission ID: 00544 Key.Net + X-Net(Lib) w/ DEGENSAC Size: 512 bytes. Matches: built-in	2039.11	194.10 Rank: 33/108	0.447 Rank: 13/108	0.814 Rank: 15/108	0.42332 (±0.00096) Rank: 42/108	312.61 Rank: 27/108	1515.67 Rank: 48/108	4.567 Rank: 27/108	0.448 Rank: 29/108	0.66559 (±0.00233) Rank: 31/108	0.54445 Rank: 32/108	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-04-28	is_submission, is_challenge_2020
Submission ID: 00031 SuperPoint (2k features, NMS=2),... Size: 1024 bytes. Matches: built-in	2048.00	105.52 Rank: 88/108	0.376 Rank: 30/108	0.624 Rank: 94/108	0.27954 (±0.00073) Rank: 90/108	130.52 Rank: 102/108	1002.03 Rank: 102/108	4.211 Rank: 71/108	0.593 Rank: 93/108	0.51061 (±0.00378) Rank: 90/108	0.39508 Rank: 90/108	Challenge organizers (contact)	superpoint-nms2-r1200	superpoint-nms2-r1200 (256 float32: 1024 bytes)	SuperPoint (initial release based on Pytorch). Lowered detection threshold to obtain up to 2k features per image. Using the built-in Non-Maxima Suppression filter at 2 pixels. Images are resized to 1200 pixels on the largest side.	https://arxiv.org/abs/1712.07629	https://github.com/MagicLeapResearch/SuperPointPretrainedNetwork	20-04-22	is_baseline
Submission ID: 00603 SuperPoint-128d-masked + SuperGl... Size: 512 bytes. Matches: custom	2041.09	404.73 Rank: 6/108	0.387 Rank: 25/108	0.774 Rank: 55/108	0.56769 (±0.00034) Rank: 3/108	415.75 Rank: 17/108	2158.03 Rank: 15/108	4.932 Rank: 12/108	0.357 Rank: 1/108	0.76987 (±0.00122) Rank: 3/108	0.66878 Rank: 3/108	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms3-refine2-r1600forcecubic-masked-d.001	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 3, confidence threshold 0.001, refinement, on 1600-pixel images). Detections on semantic classes sky and people are removed (segmentation from HFNetV2 trained on MIT ADE20K). SuperPoint descriptor, reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.2 pixel inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-30	is_submission, is_challenge_2020
Submission ID: 00622 Sift-HardNet-NM-Net_End-to-End Size: 512 bytes. Matches: custom	1892.70	152.13 Rank: 49/108	0.333 Rank: 76/108	0.807 Rank: 24/108	0.46620 (±0.00000) Rank: 18/108	156.54 Rank: 85/108	1403.97 Rank: 58/108	4.425 Rank: 38/108	0.443 Rank: 27/108	0.67160 (±0.00184) Rank: 29/108	0.56890 Rank: 22/108	Chen Zhao (contact)	siftdef	hardnet (128 float32: 512 bytes)	SIFT and HardNet, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00668 sp_ae_sg_degensac_plus Size: 512 bytes. Matches: custom	2048.00	394.88 Rank: 8/108	0.430 Rank: 19/108	0.749 Rank: 75/108	0.37510 (±0.00001) Rank: 69/108	402.56 Rank: 18/108	2246.23 Rank: 9/108	5.344 Rank: 7/108	0.432 Rank: 24/108	0.69832 (±0.00287) Rank: 22/108	0.53671 Rank: 40/108	(contact)	sid-00668-superpoint	sid-00668-superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-05-03	is_submission
Submission ID: 00667 sp_ae_sg_degensac_xp Size: 512 bytes. Matches: custom	2048.00	446.24 Rank: 4/108	0.407 Rank: 22/108	0.791 Rank: 38/108	0.58918 (±0.00001) Rank: 2/108	458.12 Rank: 6/108	2290.85 Rank: 8/108	5.123 Rank: 8/108	0.365 Rank: 3/108	0.77685 (±0.00244) Rank: 1/108	0.68301 Rank: 1/108	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-29	is_submission
Submission ID: 00023 LogPolarDesc, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	116.83 Rank: 73/108	0.323 Rank: 84/108	0.770 Rank: 60/108	0.39335 (±0.00060) Rank: 58/108	162.84 Rank: 81/108	1385.60 Rank: 62/108	4.046 Rank: 81/108	0.519 Rank: 71/108	0.57149 (±0.00344) Rank: 68/108	0.48242 Rank: 60/108	Challenge organizers (contact)	sift-def	logpolar (128 float32: 512 bytes)	LogPolarDesc descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-22	is_baseline
Submission ID: 00583 R2D2 Size: 512 bytes. Matches: built-in	2048.00	169.38 Rank: 39/108	0.398 Rank: 24/108	0.695 Rank: 84/108	0.33715 (±0.00149) Rank: 80/108	306.15 Rank: 28/108	1235.83 Rank: 83/108	4.156 Rank: 76/108	0.496 Rank: 56/108	0.60223 (±0.00103) Rank: 58/108	0.46969 Rank: 73/108	(contact)	r2d2-5k-p-aug	r2d2-5k-p-aug (128 float32: 512 bytes)		N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00001 HardNet, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	109.52 Rank: 83/108	0.323 Rank: 84/108	0.776 Rank: 52/108	0.38578 (±0.00131) Rank: 64/108	153.29 Rank: 89/108	1306.64 Rank: 72/108	4.026 Rank: 85/108	0.539 Rank: 83/108	0.55573 (±0.00175) Rank: 79/108	0.47075 Rank: 71/108	Challenge organizers (contact)	sift-def	hardnet (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline
Submission ID: 00585 NM-Net_v2 Size: 512 bytes. Matches: custom	1892.70	151.46 Rank: 51/108	0.333 Rank: 76/108	0.809 Rank: 19/108	0.46620 (±0.00000) Rank: 17/108	155.91 Rank: 87/108	1392.47 Rank: 61/108	4.424 Rank: 39/108	0.459 Rank: 35/108	0.66837 (±0.00125) Rank: 30/108	0.56729 Rank: 23/108	Chen Zhao (contact)	siftdef	hardnet (128 float32: 512 bytes)	SIFT and HardNet, followed by NM-Net_v2 and DEGENSAC.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00589 SuperPoint-128d + SuperGlue + DE... Size: 512 bytes. Matches: custom	1973.62	320.46 Rank: 15/108	0.364 Rank: 36/108	0.772 Rank: 58/108	0.55214 (±0.00098) Rank: 5/108	429.49 Rank: 15/108	2130.73 Rank: 17/108	4.570 Rank: 25/108	0.384 Rank: 11/108	0.75283 (±0.00086) Rank: 10/108	0.65248 Rank: 4/108	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms3-refine2-r1600forcecubic	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 3, refinement, on 1600-pixel images) and descriptor; reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.2 pixels inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-24	is_submission, is_challenge_2020
Submission ID: 00617 pffNet + SuperPoint + DEGENSAC Size: 512 bytes. Matches: built-in	1267.22	55.58 Rank: 107/108	0.341 Rank: 47/108	0.629 Rank: 92/108	0.25514 (±0.00059) Rank: 91/108	131.91 Rank: 100/108	893.88 Rank: 104/108	4.339 Rank: 43/108	0.534 Rank: 79/108	0.55935 (±0.00143) Rank: 77/108	0.40724 Rank: 88/108	Jongmin Lee, Seungwook Kim, Yoonwoo Jeong (contact)	superpoint	pffnet (128 float32: 512 bytes)	pffNet descriptors+ SuperPoint keypoints + DEGENSAC outlier-filtering	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00123 KeyNet-HardNet-2k Size: 512 bytes. Matches: built-in	2048.00	134.39 Rank: 62/108	0.469 Rank: 1/108	0.818 Rank: 12/108	0.32725 (±0.00026) Rank: 83/108	195.33 Rank: 62/108	1276.30 Rank: 78/108	4.493 Rank: 34/108	0.490 Rank: 55/108	0.61606 (±0.00141) Rank: 52/108	0.47166 Rank: 70/108	Challenge organizers (contact)	keynettuned	vlhardnet (128 float32: 512 bytes)	KeyNet-HardNet with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with RANSAC	N/A	N/A	20-05-03	is_baseline
Submission ID: 00008 L2-Net (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	138.44 Rank: 59/108	0.333 Rank: 60/108	0.786 Rank: 41/108	0.39980 (±0.00020) Rank: 55/108	171.93 Rank: 73/108	1333.56 Rank: 68/108	4.226 Rank: 67/108	0.496 Rank: 57/108	0.58762 (±0.00228) Rank: 62/108	0.49371 Rank: 59/108	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-22	is_baseline
Submission ID: 00647 sp_ae_sg_ransac Size: 512 bytes. Matches: custom	1873.96	252.53 Rank: 24/108	0.323 Rank: 81/108	0.663 Rank: 88/108	0.06318 (±0.00000) Rank: 106/108	256.66 Rank: 45/108	1663.09 Rank: 39/108	4.771 Rank: 16/108	0.563 Rank: 89/108	0.52605 (±0.00451) Rank: 86/108	0.29461 Rank: 95/108	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-03-29	is_submission
Submission ID: 00646 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	241.83 Rank: 29/108	0.450 Rank: 4/108	0.689 Rank: 86/108	0.32109 (±0.00043) Rank: 85/108	301.64 Rank: 29/108	1593.08 Rank: 44/108	4.285 Rank: 59/108	0.497 Rank: 59/108	0.60774 (±0.00266) Rank: 54/108	0.46442 Rank: 76/108	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features score-wise. HardNet, pre-trained on all datasets, is used as a descriptor head. Graph-Cut(GC)-RANSAC is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-28	is_submission
Submission ID: 00509 Upright-Sift + X-Net-lib w/ DEGE... Size: 512 bytes. Matches: built-in	1892.70	116.55 Rank: 76/108	0.333 Rank: 80/108	0.821 Rank: 9/108	0.42382 (±0.00089) Rank: 41/108	168.67 Rank: 77/108	1319.63 Rank: 70/108	4.303 Rank: 55/108	0.468 Rank: 45/108	0.62953 (±0.00216) Rank: 47/108	0.52668 Rank: 46/108	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	sift-def	x-net-lib-upright-no-dups (128 float32: 512 bytes)		N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00658 disk_degree_end-to-end Size: 512 bytes. Matches: custom	2048.00	346.06 Rank: 12/108	0.447 Rank: 15/108	0.843 Rank: 2/108	0.51061 (±0.00000) Rank: 12/108	354.06 Rank: 22/108	2170.03 Rank: 12/108	5.679 Rank: 1/108	0.393 Rank: 12/108	0.74934 (±0.00266) Rank: 12/108	0.62998 Rank: 12/108	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree_End-to-End and DEGENSAC.	N/A	N/A	21-04-22	is_submission
Submission ID: 00665 sp_degree(patch)_degensac-1.1 Size: 1024 bytes. Matches: custom	2048.00	257.76 Rank: 23/108	0.360 Rank: 38/108	0.702 Rank: 83/108	0.41739 (±0.00000) Rank: 48/108	264.72 Rank: 43/108	1659.66 Rank: 40/108	5.017 Rank: 11/108	0.421 Rank: 20/108	0.70462 (±0.00152) Rank: 17/108	0.56100 Rank: 27/108	Weiyue Zhao (contact)	superpoint	superpoint (256 float32: 1024 bytes)	superpoint discriptors, followed by degree(patch)_End-to-End and DEGENSAC th=1.1	N/A	N/A	21-04-29	is_submission
Submission ID: 00605 guided-hardnet-epoch2-v2 Size: 512 bytes. Matches: custom	2047.76	220.88 Rank: 30/108	0.347 Rank: 43/108	0.778 Rank: 50/108	0.46469 (±0.00000) Rank: 19/108	226.84 Rank: 53/108	1827.60 Rank: 26/108	4.512 Rank: 31/108	0.427 Rank: 22/108	0.69306 (±0.00160) Rank: 24/108	0.57888 Rank: 20/108	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet and guided matching and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00659 superpoint_degree_end-to-end Size: 1024 bytes. Matches: custom	2048.00	107.59 Rank: 84/108	0.360 Rank: 38/108	0.797 Rank: 34/108	0.02095 (±0.00000) Rank: 108/108	111.13 Rank: 108/108	862.34 Rank: 106/108	5.026 Rank: 10/108	0.700 Rank: 97/108	0.39130 (±0.00532) Rank: 95/108	0.20613 Rank: 98/108	Weiyue Zhao (contact)	superpoint	superpoint (256 float32: 1024 bytes)	superpoint discriptors, followed by degree_End-to-End and DEGENSAC.	N/A	N/A	21-04-23	is_submission
Submission ID: 00645 sp_ae_sg Size: 512 bytes. Matches: custom	1873.96	488.91 Rank: 2/108	0.323 Rank: 81/108	0.585 Rank: 96/108	0.28532 (±0.00000) Rank: 89/108	498.60 Rank: 3/108	2432.22 Rank: 2/108	4.542 Rank: 29/108	0.399 Rank: 14/108	0.72857 (±0.00361) Rank: 14/108	0.50694 Rank: 53/108	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-03-18	is_submission
Submission ID: 00527 SEKD Size: 512 bytes. Matches: built-in	1786.69	106.34 Rank: 86/108	0.383 Rank: 29/108	0.759 Rank: 71/108	0.38886 (±0.00013) Rank: 59/108	150.11 Rank: 91/108	1063.08 Rank: 100/108	4.575 Rank: 23/108	0.465 Rank: 42/108	0.63303 (±0.00244) Rank: 45/108	0.51095 Rank: 51/108	Yafei Song, Ling Cai, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	The name of our method is Self-Evolving Keypoint Detection and Description (SEKD). Now, the SEKD model is trained only using COCO test images. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-27	is_submission, is_challenge_2020
Submission ID: ????? CV-DoG-HardNet8-PT Size: 512 bytes. Matches: built-in	2048.00	114.84 Rank: 79/108	0.321 Rank: 97/108	0.790 Rank: 39/108	0.38200 (±0.00081) Rank: 66/108	117.84 Rank: 107/108	1067.13 Rank: 99/108	4.051 Rank: 80/108	0.531 Rank: 77/108	0.53035 (±0.00531) Rank: 84/108	0.45617 Rank: 79/108	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift2k	h8e512pt (128 float32: 512 bytes)	[sid:00593] HardNet8 with PCA compression	N/A	N/A	20-05-27	is_submission, is_challenge_2020
Submission ID: 00044 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1892.72	104.22 Rank: 90/108	0.333 Rank: 56/108	0.763 Rank: 68/108	0.37078 (±0.00056) Rank: 71/108	205.17 Rank: 57/108	1300.49 Rank: 74/108	4.000 Rank: 91/108	0.563 Rank: 88/108	0.52549 (±0.00120) Rank: 87/108	0.44814 Rank: 81/108	Challenge organizers (contact)	sift-def	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00004 SOSNet (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	176.54 Rank: 35/108	0.333 Rank: 60/108	0.782 Rank: 44/108	0.43803 (±0.00092) Rank: 35/108	183.92 Rank: 67/108	1403.66 Rank: 59/108	4.314 Rank: 50/108	0.474 Rank: 50/108	0.62181 (±0.00161) Rank: 50/108	0.52992 Rank: 45/108	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00043 DELF-GLD (128D), PyRANSAC Size: 512 bytes. Matches: built-in	2036.82	94.70 Rank: 95/108	0.109 Rank: 102/108	0.156 Rank: 104/108	0.07298 (±0.00056) Rank: 104/108	430.68 Rank: 13/108	2162.72 Rank: 13/108	2.535 Rank: 104/108	0.887 Rank: 105/108	0.13247 (±0.00116) Rank: 103/108	0.10272 Rank: 104/108	Challenge organizers (contact)	delf-gld-2k-128d	delf-gld-2k-128d (128 float32: 512 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 128 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-23	is_baseline
Submission ID: 00612 SuperPoint-128d-adapt + SuperGlu... Size: 512 bytes. Matches: custom	2048.00	441.49 Rank: 5/108	0.407 Rank: 21/108	0.789 Rank: 40/108	0.59034 (±0.00050) Rank: 1/108	452.99 Rank: 9/108	2245.43 Rank: 10/108	5.092 Rank: 9/108	0.358 Rank: 2/108	0.77337 (±0.00167) Rank: 2/108	0.68186 Rank: 2/108	Paul-Edouard Sarlin (contact)	superpoint-k2048-nms4-refine2-r1600forcecubic-masked-d.001-adapt50	superpoint-down128 (128 float32: 512 bytes)	SuperPoint detector (2048 keypoints, NMS with radius 4, confidence threshold 0.001, refinement, on 1600-pixel images). The detection heatmap is improved with test-time homographic adaptation (50 iterations), and detections on semantic classes sky and people are removed (segmentation from HFNetV2 trained on MIT ADE20K). SuperPoint descriptor, reduced to 128d with a linear autoencoder. SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.1 pixel inlier threshold).	https://arxiv.org/abs/1911.11763	https://psarlin.com/superglue	20-05-31	is_submission, is_challenge_2020
Submission ID: 00615 SIFT-Fusion_Max-NM-Net_End-to-En... Size: 512 bytes. Matches: custom	1892.70	115.46 Rank: 77/108	0.333 Rank: 76/108	0.783 Rank: 43/108	0.40244 (±0.00000) Rank: 53/108	119.23 Rank: 106/108	1167.54 Rank: 91/108	4.367 Rank: 41/108	0.489 Rank: 54/108	0.60720 (±0.00114) Rank: 55/108	0.50482 Rank: 54/108	Chen Zhao (contact)	siftdef	fusion-max (128 float32: 512 bytes)	SIFT and Fusion_Max, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00002 SURF (OpenCV), DEGENSAC Size: 256 bytes. Matches: built-in	2010.88	71.90 Rank: 102/108	0.321 Rank: 96/108	0.634 Rank: 89/108	0.20761 (±0.00029) Rank: 94/108	300.02 Rank: 32/108	1208.06 Rank: 88/108	3.549 Rank: 97/108	0.695 Rank: 96/108	0.37845 (±0.00399) Rank: 97/108	0.29303 Rank: 96/108	Challenge organizers (contact)	surf-def	surf (64 float32: 256 bytes)	SURF with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00641 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	219.72 Rank: 31/108	0.450 Rank: 4/108	0.722 Rank: 81/108	0.34184 (±0.00032) Rank: 78/108	301.64 Rank: 29/108	1595.30 Rank: 43/108	4.278 Rank: 62/108	0.499 Rank: 60/108	0.60963 (±0.00152) Rank: 53/108	0.47574 Rank: 66/108	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. MAGSAC is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-04	is_submission
Submission ID: 00705 Upright SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1892.72	104.74 Rank: 89/108	0.333 Rank: 56/108	0.764 Rank: 67/108	0.37292 (±0.00081) Rank: 70/108	204.13 Rank: 58/108	1293.75 Rank: 75/108	4.008 Rank: 88/108	0.567 Rank: 91/108	0.52808 (±0.00416) Rank: 85/108	0.45050 Rank: 80/108	Challenge organizers (contact)	sift-def	sift-upright (128 float32: 512 bytes)	Upright SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-06-01	is_baseline
Submission ID: 00014 L2-Net, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	107.38 Rank: 85/108	0.323 Rank: 90/108	0.766 Rank: 64/108	0.35649 (±0.00110) Rank: 75/108	138.97 Rank: 98/108	1211.49 Rank: 85/108	3.948 Rank: 92/108	0.538 Rank: 82/108	0.52019 (±0.00120) Rank: 88/108	0.43834 Rank: 83/108	Challenge organizers (contact)	sift-def	l2net (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00507 sift and hardnet512 Size: 2048 bytes. Matches: built-in	2000.00	98.07 Rank: 93/108	0.321 Rank: 98/108	0.778 Rank: 49/108	0.35047 (±0.00046) Rank: 76/108	153.92 Rank: 88/108	1310.53 Rank: 71/108	4.017 Rank: 87/108	0.515 Rank: 68/108	0.56598 (±0.00185) Rank: 72/108	0.45822 Rank: 78/108	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift8k	hardnet512 (512 float32: 2048 bytes)	SIFT with 2000 keypoints, hardnet512 with 512 descriptors	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00032 Root-SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1936.30	83.46 Rank: 100/108	0.323 Rank: 94/108	0.756 Rank: 73/108	0.33605 (±0.00092) Rank: 81/108	167.84 Rank: 78/108	1141.88 Rank: 94/108	3.788 Rank: 95/108	0.576 Rank: 92/108	0.46826 (±0.00154) Rank: 93/108	0.40216 Rank: 89/108	Challenge organizers (contact)	sift-def	rootsift (128 float32: 512 bytes)	Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00041 SuperPoint (2k features, NMS=3),... Size: 1024 bytes. Matches: built-in	2048.00	117.13 Rank: 71/108	0.368 Rank: 31/108	0.628 Rank: 93/108	0.28679 (±0.00056) Rank: 88/108	156.04 Rank: 86/108	1121.20 Rank: 96/108	4.310 Rank: 54/108	0.566 Rank: 90/108	0.53829 (±0.00021) Rank: 82/108	0.41254 Rank: 87/108	Challenge organizers (contact)	superpoint-nms3-r1200	superpoint-nms3-r1200 (256 float32: 1024 bytes)	SuperPoint (initial release based on Pytorch). Lowered detection threshold to obtain up to 2k features per image. Using the built-in Non-Maxima Suppression filter at 3 pixels. Images are resized to 1200 pixels on the largest side.	https://arxiv.org/abs/1712.07629	https://github.com/MagicLeapResearch/SuperPointPretrainedNetwork	20-04-23	is_baseline
Submission ID: 00007 L2-Net, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	87.04 Rank: 99/108	0.323 Rank: 90/108	0.779 Rank: 48/108	0.36265 (±0.00090) Rank: 72/108	138.97 Rank: 98/108	1211.49 Rank: 85/108	3.948 Rank: 92/108	0.537 Rank: 80/108	0.52019 (±0.00120) Rank: 88/108	0.44142 Rank: 82/108	Challenge organizers (contact)	sift-def	l2net (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00599 SEKD Size: 512 bytes. Matches: built-in	2043.44	129.46 Rank: 65/108	0.386 Rank: 26/108	0.809 Rank: 20/108	0.45066 (±0.00057) Rank: 26/108	176.60 Rank: 71/108	1209.63 Rank: 87/108	4.437 Rank: 36/108	0.454 Rank: 32/108	0.66094 (±0.00227) Rank: 33/108	0.55580 Rank: 28/108	Yafei Song, Ling Cai, Jia Li, Yonghong Tian, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	Our method named SEKD: Self-Evolving Keypoint Detection and Description, where the SEKD model is trained using COCO validation set. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, without flann, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-05-29	is_submission, is_challenge_2020
Submission ID: 00136 CV-DoG-AffNet-HardNet-kornia-MAG... Size: 512 bytes. Matches: built-in	2047.84	195.17 Rank: 32/108	0.339 Rank: 49/108	0.774 Rank: 54/108	0.41747 (±0.00072) Rank: 47/108	284.06 Rank: 38/108	1788.70 Rank: 30/108	4.191 Rank: 72/108	0.511 Rank: 66/108	0.58536 (±0.00125) Rank: 64/108	0.50141 Rank: 56/108	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00011 GeoDesc, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	93.44 Rank: 96/108	0.323 Rank: 90/108	0.759 Rank: 70/108	0.34215 (±0.00054) Rank: 77/108	120.68 Rank: 104/108	1098.70 Rank: 97/108	4.002 Rank: 89/108	0.552 Rank: 86/108	0.50594 (±0.00073) Rank: 91/108	0.42405 Rank: 84/108	Challenge organizers (contact)	sift-def	geodesc (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00018 R2D2 (r2d2-wasf-n16), DEGENSAC Size: 512 bytes. Matches: built-in	2048.00	176.49 Rank: 36/108	0.422 Rank: 20/108	0.725 Rank: 80/108	0.35783 (±0.00061) Rank: 74/108	262.02 Rank: 44/108	1188.11 Rank: 89/108	4.299 Rank: 58/108	0.499 Rank: 61/108	0.60639 (±0.00124) Rank: 56/108	0.48211 Rank: 61/108	Challenge organizers (contact)	r2d2-wasf-n16	r2d2-wasf-n16 (128 float32: 512 bytes)	R2D2 (model r2d2-wasf-n16) with 2k features, using the built-in matcher (bidirectional filter with the both strategy) and no ratio test, and DEGENSAC with optimal parameters for stereo.	N/A	https://opencv.org	20-04-23	is_baseline
Submission ID: ????? CV-DoG-HardNet8-PTv2 Size: 512 bytes. Matches: built-in	2047.77	153.69 Rank: 47/108	0.339 Rank: 53/108	0.821 Rank: 8/108	0.43869 (±0.00017) Rank: 32/108	158.63 Rank: 84/108	1221.12 Rank: 84/108	4.350 Rank: 42/108	0.484 Rank: 53/108	0.60206 (±0.00003) Rank: 59/108	0.52038 Rank: 48/108	Milan Pultar, Dmytro Mishkin, Jiri Matas (contact)	sift2k	h8e512pt (128 float32: 512 bytes)	[sid:00619] HardNet8 with PCA compression, batch sampling from few images	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00025 DELF-GLD (32D), DEGENSAC Size: 128 bytes. Matches: built-in	2036.82	96.56 Rank: 94/108	0.109 Rank: 102/108	0.119 Rank: 107/108	0.06573 (±0.00030) Rank: 105/108	453.19 Rank: 7/108	1805.48 Rank: 27/108	2.425 Rank: 106/108	0.920 Rank: 108/108	0.09163 (±0.00140) Rank: 106/108	0.07868 Rank: 106/108	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00578 Key.Net-s + DescNet w/ DEGENSAC Size: 512 bytes. Matches: built-in	2033.67	246.62 Rank: 26/108	0.449 Rank: 9/108	0.805 Rank: 25/108	0.45418 (±0.00056) Rank: 24/108	331.55 Rank: 24/108	1621.68 Rank: 42/108	4.570 Rank: 26/108	0.447 Rank: 28/108	0.67414 (±0.00371) Rank: 28/108	0.56416 Rank: 24/108	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet-s	descnet (128 float32: 512 bytes)		N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00016 LogPolarDesc, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	148.56 Rank: 53/108	0.323 Rank: 84/108	0.752 Rank: 74/108	0.38840 (±0.00064) Rank: 60/108	162.84 Rank: 81/108	1385.60 Rank: 62/108	4.046 Rank: 81/108	0.516 Rank: 69/108	0.57149 (±0.00344) Rank: 68/108	0.47994 Rank: 63/108	Challenge organizers (contact)	sift-def	logpolar (128 float32: 512 bytes)	LogPolarDesc descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-04-23	is_baseline
Submission ID: 00704 SOSNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	171.22 Rank: 38/108	0.333 Rank: 60/108	0.804 Rank: 26/108	0.45053 (±0.00053) Rank: 27/108	194.04 Rank: 63/108	1442.32 Rank: 54/108	4.313 Rank: 52/108	0.467 Rank: 44/108	0.63586 (±0.00246) Rank: 43/108	0.54320 Rank: 33/108	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-06-02	is_baseline
Submission ID: 00604 Key.Net+GIFT+GMCNet+DEGENSAC Size: 512 bytes. Matches: custom	2046.07	377.20 Rank: 9/108	0.450 Rank: 8/108	0.776 Rank: 51/108	0.45282 (±0.00000) Rank: 25/108	386.63 Rank: 19/108	1474.77 Rank: 51/108	4.663 Rank: 21/108	0.420 Rank: 17/108	0.70500 (±0.00294) Rank: 16/108	0.57891 Rank: 19/108	Yuan Liu (contact)	keynet-2k	scale-gift (128 float32: 512 bytes)	Detecting by Key.Net, descriptors from GIFT, matching by Graph Motion Coherence Network, geometry estimated by DEGENSAC with inlier threshold 0.7	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00700 HardNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	152.69 Rank: 48/108	0.333 Rank: 60/108	0.812 Rank: 17/108	0.46093 (±0.00080) Rank: 21/108	201.26 Rank: 59/108	1467.86 Rank: 53/108	4.311 Rank: 53/108	0.466 Rank: 43/108	0.63544 (±0.00324) Rank: 44/108	0.54819 Rank: 30/108	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-06-01	is_baseline
Submission ID: 00036 SOSNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	155.75 Rank: 45/108	0.333 Rank: 60/108	0.794 Rank: 36/108	0.43077 (±0.00017) Rank: 38/108	183.92 Rank: 67/108	1403.66 Rank: 59/108	4.314 Rank: 50/108	0.472 Rank: 47/108	0.62181 (±0.00161) Rank: 50/108	0.52629 Rank: 47/108	Challenge organizers (contact)	sift-def	sosnet-upright (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00020 D2-Net (multi-scale), DEGENSAC Size: 2048 bytes. Matches: built-in	2045.55	118.41 Rank: 70/108	0.168 Rank: 101/108	0.293 Rank: 101/108	0.12270 (±0.00048) Rank: 98/108	286.10 Rank: 36/108	1999.37 Rank: 19/108	3.013 Rank: 101/108	0.774 Rank: 99/108	0.28297 (±0.00254) Rank: 99/108	0.20283 Rank: 100/108	Challenge organizers (contact)	d2net-multiscale	d2net-multiscale (512 float32: 2048 bytes)	D2-Net, multi-scale model, up to 2048 features. Trained on the MegaDepth dataset, removing scenes which overlap with the Phototourism test set. Stereo with DEGENSAC and optimal parameters. Parameters tuned separately from the 8k submission.	http://openaccess.thecvf.com/content_CVPR_2019/papers/Dusmanu_D2-Net_A_Trainable_CNN_for_Joint_Description_and_Detection_of_CVPR_2019_paper.pdf	https://github.com/mihaidusmanu/d2-net	20-04-23	is_baseline
Submission ID: 00021 DELF-GLD (512D), PyRANSAC Size: 2048 bytes. Matches: built-in	2036.82	92.96 Rank: 97/108	0.109 Rank: 102/108	0.175 Rank: 102/108	0.07687 (±0.00010) Rank: 103/108	437.64 Rank: 11/108	2296.72 Rank: 6/108	2.567 Rank: 102/108	0.878 Rank: 103/108	0.14991 (±0.00513) Rank: 101/108	0.11339 Rank: 102/108	Challenge organizers (contact)	delf-gld-2k-512d	delf-gld-2k-512d (512 float32: 2048 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 512 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00575 SIFT2k-2048-HardNet64-rain-all-s... Size: 512 bytes. Matches: built-in	2047.77	161.77 Rank: 41/108	0.339 Rank: 52/108	0.814 Rank: 13/108	0.43543 (±0.00051) Rank: 36/108	255.45 Rank: 46/108	1708.29 Rank: 36/108	4.327 Rank: 48/108	0.451 Rank: 31/108	0.64653 (±0.00221) Rank: 36/108	0.54098 Rank: 34/108	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2048-hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 2048 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-20	is_submission, is_challenge_2020
Submission ID: 00531 Key.Net + X-Net w/ DEGENSAC Size: 512 bytes. Matches: built-in	2040.08	173.65 Rank: 37/108	0.447 Rank: 12/108	0.797 Rank: 33/108	0.38608 (±0.00068) Rank: 63/108	276.91 Rank: 42/108	1468.50 Rank: 52/108	4.447 Rank: 35/108	0.463 Rank: 39/108	0.64369 (±0.00081) Rank: 39/108	0.51489 Rank: 50/108	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet	x-net (128 float32: 512 bytes)		N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00515 SuperPoint + SuperGlue + DEGENSA... Size: 1024 bytes. Matches: custom	1973.62	320.50 Rank: 14/108	0.364 Rank: 35/108	0.772 Rank: 57/108	0.55234 (±0.00024) Rank: 4/108	429.48 Rank: 16/108	2131.07 Rank: 16/108	4.572 Rank: 24/108	0.373 Rank: 7/108	0.75213 (±0.00345) Rank: 11/108	0.65224 Rank: 5/108	Paul-Edouard Sarlin (contact)	superpoint-nms3-refine2-r1600forcecubic	superpoint (256 float32: 1024 bytes)	SuperPoint detector and descriptor (2048 keypoints, NMS with radius 3, refinement, on 1600-pixel images). SuperGlue matcher (outdoor model, 150 Sinkhorn iterations). For stereo, DEGENSAC model estimator (1.2 pixels inlier threshold).	https://arxiv.org/abs/1911.11763	https://github.com/magicleap/SuperGluePretrainedNetwork	20-04-24	is_submission, is_challenge_2020
Submission ID: 00565 SIFT2k_2000_HardNet64-train-all-... Size: 512 bytes. Matches: built-in	1999.83	157.70 Rank: 43/108	0.336 Rank: 54/108	0.813 Rank: 16/108	0.43188 (±0.00005) Rank: 37/108	248.91 Rank: 51/108	1666.27 Rank: 38/108	4.323 Rank: 49/108	0.465 Rank: 41/108	0.64404 (±0.00271) Rank: 38/108	0.53796 Rank: 38/108	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2000-hardnet64-train-all-sos-812000 (128 float32: 512 bytes)	SIFT with 2000 keypoints(scale 12), sosnet64 with 128 descriptors(trained with sos loss and step 812000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00512 Hardnet with DEGENSAC for stereo Size: 512 bytes. Matches: built-in	1999.50	51.91 Rank: 108/108	0.172 Rank: 100/108	0.558 Rank: 98/108	0.20771 (±0.00049) Rank: 93/108	181.48 Rank: 69/108	1692.63 Rank: 37/108	3.050 Rank: 100/108	0.695 Rank: 95/108	0.38463 (±0.00275) Rank: 96/108	0.29617 Rank: 94/108	Vu Trung Nghia & Nguyen Trung Hieu (contact)	siftdef	hardnet-64 (128 float32: 512 bytes)	Using hardnet network to embed a patch (32 x 32) to a 128 (float32) dimensions vector, For stereo, we use DEGENSAC (Chum et al, CVPR'05) with optimal settings	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00012 GeoDesc (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	144.92 Rank: 55/108	0.333 Rank: 60/108	0.765 Rank: 66/108	0.38124 (±0.00056) Rank: 67/108	149.66 Rank: 92/108	1237.96 Rank: 81/108	4.219 Rank: 69/108	0.526 Rank: 76/108	0.56687 (±0.00261) Rank: 70/108	0.47405 Rank: 67/108	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00038 SOSNet, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	134.59 Rank: 61/108	0.323 Rank: 84/108	0.760 Rank: 69/108	0.38654 (±0.00056) Rank: 62/108	145.47 Rank: 96/108	1271.41 Rank: 79/108	4.045 Rank: 83/108	0.526 Rank: 75/108	0.56072 (±0.00077) Rank: 75/108	0.47363 Rank: 68/108	Challenge organizers (contact)	sift-def	sosnet (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00582 R2D2 Size: 512 bytes. Matches: built-in	2048.00	155.21 Rank: 46/108	0.403 Rank: 23/108	0.709 Rank: 82/108	0.32501 (±0.00085) Rank: 84/108	285.11 Rank: 37/108	1162.94 Rank: 93/108	4.161 Rank: 75/108	0.508 Rank: 63/108	0.60276 (±0.00062) Rank: 57/108	0.46389 Rank: 77/108	(contact)	r2d2-5k-p	r2d2-5k-p (128 float32: 512 bytes)		N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00555 SuperPoint+GIFT+Graph Motion Coh... Size: 512 bytes. Matches: custom	1940.85	281.06 Rank: 20/108	0.356 Rank: 40/108	0.676 Rank: 87/108	0.42283 (±0.00000) Rank: 43/108	290.06 Rank: 34/108	1887.70 Rank: 25/108	4.567 Rank: 28/108	0.420 Rank: 18/108	0.70395 (±0.00239) Rank: 18/108	0.56339 Rank: 25/108	Yuan Liu (contact)	superpoint-2k	scale-gift (128 float32: 512 bytes)	Detecting by SuperPoint, descriptors from GIFT, matching by Graph Motion Coherence Network, geometry estimated by DEGENSAC with inlier threshold 1.0	N/A	N/A	20-05-10	is_submission, is_challenge_2020
Submission ID: 00657 pretrained_sg_DEGENSAC Size: 1024 bytes. Matches: custom	1949.08	306.63 Rank: 16/108	0.365 Rank: 33/108	0.769 Rank: 61/108	0.54217 (±0.00000) Rank: 8/108	314.15 Rank: 25/108	1756.58 Rank: 34/108	4.808 Rank: 14/108	0.369 Rank: 5/108	0.76221 (±0.00290) Rank: 5/108	0.65219 Rank: 6/108	(contact)	superglue	superglue (256 float32: 1024 bytes)	pretrained outdoor superglue	N/A	N/A	21-04-24	is_submission
Submission ID: 00640 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: built-in	2048.00	128.76 Rank: 66/108	0.450 Rank: 4/108	0.744 Rank: 77/108	0.33122 (±0.00042) Rank: 82/108	289.47 Rank: 35/108	1543.70 Rank: 47/108	4.284 Rank: 60/108	0.509 Rank: 64/108	0.60128 (±0.00311) Rank: 60/108	0.46625 Rank: 75/108	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. DEGENSA with degeneracy check on is used as a robust estimator. Cyclic consistency matching with a threshold of 0.95 is used.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-02-17	is_submission
Submission ID: 00039 HardNet, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	138.43 Rank: 60/108	0.323 Rank: 84/108	0.757 Rank: 72/108	0.38490 (±0.00120) Rank: 65/108	153.29 Rank: 89/108	1306.64 Rank: 72/108	4.026 Rank: 85/108	0.537 Rank: 81/108	0.55573 (±0.00175) Rank: 79/108	0.47032 Rank: 72/108	Challenge organizers (contact)	sift-def	hardnet (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00516 Key.Net (MS 4-1, Pyramid) + X-Ne... Size: 512 bytes. Matches: built-in	2039.11	265.52 Rank: 22/108	0.447 Rank: 13/108	0.776 Rank: 53/108	0.40559 (±0.00112) Rank: 51/108	336.81 Rank: 23/108	1559.74 Rank: 45/108	4.536 Rank: 30/108	0.461 Rank: 37/108	0.66406 (±0.00521) Rank: 32/108	0.53483 Rank: 41/108	Barroso-Laguna, Axel and Tian, Yurun and Ng, Tony (contact)	keynet-41-pyramidv2	x-net-lib (128 float32: 512 bytes)		N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00517 retrained Hardnet with MAGSAC Size: 512 bytes. Matches: built-in	1892.71	133.39 Rank: 63/108	0.333 Rank: 60/108	0.808 Rank: 22/108	0.40042 (±0.00077) Rank: 54/108	210.10 Rank: 56/108	1480.67 Rank: 50/108	4.226 Rank: 66/108	0.479 Rank: 51/108	0.63030 (±0.00242) Rank: 46/108	0.51536 Rank: 49/108	Vu Trung Nghia and Nguyen Trung Hieu (contact)	sift-upright	hardnet (128 float32: 512 bytes)	Using SIFT upright and retrained Hardnet for keypoints and descriptors. For stereo, we use DEGENSAC Chum et al, CVPR05 with optimal settings	N/A	N/A	20-04-24	is_submission, is_challenge_2020
Submission ID: 00711 DISK-32D (depth) Size: 128 bytes. Matches: built-in	2048.00	341.83 Rank: 13/108	0.448 Rank: 10/108	0.842 Rank: 3/108	0.47005 (±0.00037) Rank: 15/108	441.16 Rank: 10/108	2231.55 Rank: 11/108	5.524 Rank: 6/108	0.415 Rank: 16/108	0.70220 (±0.00208) Rank: 19/108	0.58613 Rank: 16/108	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048	disk-2020-09-15-nms-7-depth-32-save-46-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with depth-based constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 7x7 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00009 GeoDesc (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	116.56 Rank: 75/108	0.333 Rank: 60/108	0.784 Rank: 42/108	0.38660 (±0.00078) Rank: 61/108	149.66 Rank: 92/108	1237.96 Rank: 81/108	4.219 Rank: 69/108	0.525 Rank: 74/108	0.56687 (±0.00261) Rank: 70/108	0.47673 Rank: 65/108	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-22	is_baseline
Submission ID: 00623 HardNet-epoch2-OANet Size: 512 bytes. Matches: custom	2047.76	247.45 Rank: 25/108	0.347 Rank: 43/108	0.765 Rank: 65/108	0.44717 (±0.00000) Rank: 29/108	254.11 Rank: 48/108	1898.45 Rank: 24/108	4.503 Rank: 32/108	0.437 Rank: 26/108	0.67958 (±0.00183) Rank: 26/108	0.56337 Rank: 26/108	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet trained from scratch and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-06-02	is_submission, is_challenge_2020
Submission ID: 00670 superglue_adapt_degensac Size: 1024 bytes. Matches: custom	2048.00	365.61 Rank: 10/108	0.383 Rank: 28/108	0.747 Rank: 76/108	0.50988 (±0.00000) Rank: 13/108	376.42 Rank: 20/108	2076.46 Rank: 18/108	4.905 Rank: 13/108	0.373 Rank: 6/108	0.76226 (±0.00085) Rank: 4/108	0.63607 Rank: 11/108	Downey (contact)	superpointadapt30	superglueadapt30 (256 float32: 1024 bytes)	degensac t1.2 adaption30	N/A	N/A	21-05-07	is_submission
Submission ID: 00620 pffNet + SuperPoint + MAGSAC Size: 512 bytes. Matches: built-in	1267.22	57.48 Rank: 106/108	0.341 Rank: 47/108	0.632 Rank: 90/108	0.22549 (±0.00077) Rank: 92/108	131.91 Rank: 100/108	893.88 Rank: 104/108	4.339 Rank: 43/108	0.531 Rank: 78/108	0.55935 (±0.00143) Rank: 77/108	0.39242 Rank: 91/108	Jongmin Lee, Seungwook Kim, Yoonwoo Jeong (contact)	superpoint	pffnet (128 float32: 512 bytes)	pffNet descriptors + SuperPoint keypoints + MAGSAC outlier-filtering	N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00135 CV-DoG-AffNet-HardNet-kornia-PyR... Size: 512 bytes. Matches: built-in	2047.84	105.57 Rank: 87/108	0.339 Rank: 49/108	0.800 Rank: 30/108	0.35887 (±0.00090) Rank: 73/108	284.06 Rank: 38/108	1788.70 Rank: 30/108	4.191 Rank: 72/108	0.513 Rank: 67/108	0.58536 (±0.00125) Rank: 64/108	0.47211 Rank: 69/108	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00026 AKAZE (OpenCV), DEGENSAC Size: 61 bytes. Matches: built-in	1657.83	69.77 Rank: 104/108	0.355 Rank: 41/108	0.622 Rank: 95/108	0.17632 (±0.00087) Rank: 96/108	224.40 Rank: 54/108	795.54 Rank: 107/108	3.311 Rank: 99/108	0.819 Rank: 100/108	0.23020 (±0.00466) Rank: 100/108	0.20326 Rank: 99/108	Challenge organizers (contact)	akaze-def	akaze (61 uint8: 61 bytes)	AKAZE with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00706 LogPolar-Upright w/ DEGENSAC (no... Size: 512 bytes. Matches: built-in	1892.71	162.20 Rank: 40/108	0.333 Rank: 60/108	0.807 Rank: 23/108	0.45674 (±0.00059) Rank: 23/108	211.87 Rank: 55/108	1553.36 Rank: 46/108	4.331 Rank: 46/108	0.471 Rank: 46/108	0.63701 (±0.00278) Rank: 42/108	0.54688 Rank: 31/108	Challenge organizers (contact)	sift-def	logpolar-upright (128 float32: 512 bytes)	Upright LogPolar descriptors on DoG features (OpenCV). Using the built-in matcher: bidirectional filter with the both strategy. DEGENSAC for stereo. FLANN disabled.	https://arxiv.org/abs/1908.05547	https://github.com/cvlab-epfl/log-polar-descriptors	20-06-02	is_baseline
Submission ID: 00669 disk_degree(refine)_End-to-End Size: 512 bytes. Matches: custom	2048.00	591.03 Rank: 1/108	0.447 Rank: 15/108	0.811 Rank: 18/108	0.53291 (±0.00000) Rank: 10/108	602.70 Rank: 1/108	2633.91 Rank: 1/108	5.556 Rank: 4/108	0.382 Rank: 10/108	0.75413 (±0.00231) Rank: 9/108	0.64352 Rank: 10/108	Weiyue Zhao (contact)	disk	disk (128 float32: 512 bytes)	disk discriptors, followed by degree(refine)_End-to-End and DEGENSAC.	N/A	N/A	21-05-07	is_submission
Submission ID: 00662 retrained_superglue_DEGENSAC Size: 1024 bytes. Matches: custom	1949.08	306.63 Rank: 16/108	0.365 Rank: 33/108	0.769 Rank: 61/108	0.54220 (±0.00000) Rank: 7/108	314.15 Rank: 25/108	1757.90 Rank: 33/108	4.805 Rank: 15/108	0.375 Rank: 9/108	0.76055 (±0.00173) Rank: 6/108	0.65137 Rank: 8/108	(contact)	superglue	superglue (256 float32: 1024 bytes)	pretrained outdoor superglue	N/A	N/A	21-04-25	is_submission
Submission ID: 00022 L2-Net (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	110.72 Rank: 82/108	0.333 Rank: 60/108	0.801 Rank: 29/108	0.40729 (±0.00097) Rank: 50/108	171.93 Rank: 73/108	1333.56 Rank: 68/108	4.226 Rank: 67/108	0.497 Rank: 58/108	0.58762 (±0.00228) Rank: 62/108	0.49746 Rank: 58/108	Challenge organizers (contact)	sift-def	l2net-upright (128 float32: 512 bytes)	L2-Net descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	http://www.nlpr.ia.ac.cn/fanbin/pub/L2-Net_CVPR17.pdf	https://github.com/vcg-uvic/image-matching-benchmark-baselines	20-04-23	is_baseline
Submission ID: 00606 HardNet-epoch2-OANet Size: 512 bytes. Matches: custom	2047.76	244.62 Rank: 28/108	0.347 Rank: 43/108	0.773 Rank: 56/108	0.46389 (±0.00000) Rank: 20/108	251.11 Rank: 50/108	1916.44 Rank: 23/108	4.501 Rank: 33/108	0.428 Rank: 23/108	0.69407 (±0.00216) Rank: 23/108	0.57898 Rank: 18/108	Chen Shen, Zhipeng Wang, Jun Zhang, Zhongkun Chen, Zhiwei Ruan, Jingchao Zhou, Pengfei Xu (contact)	sift2k	hardnet-epoch2 (128 float32: 512 bytes)	sift and hardnet with 2k features, using the oanet and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-05-30	is_submission, is_challenge_2020
Submission ID: 00013 SIFT (OpenCV), DEGENSAC Size: 512 bytes. Matches: built-in	1936.30	76.94 Rank: 101/108	0.323 Rank: 94/108	0.733 Rank: 79/108	0.30766 (±0.00075) Rank: 86/108	171.92 Rank: 75/108	1059.70 Rank: 101/108	3.687 Rank: 96/108	0.618 Rank: 94/108	0.42476 (±0.00103) Rank: 94/108	0.36621 Rank: 92/108	Challenge organizers (contact)	sift-def	sift (128 float32: 512 bytes)	SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-23	is_baseline
Submission ID: 00513 Personal Inplementation SIFT (GP... Size: 512 bytes. Matches: built-in	1900.60	70.04 Rank: 103/108	0.085 Rank: 108/108	0.002 Rank: 108/108	0.07782 (±0.00022) Rank: 102/108	173.06 Rank: 72/108	948.78 Rank: 103/108	3.816 Rank: 94/108	0.887 Rank: 106/108	0.09835 (±0.00068) Rank: 105/108	0.08809 Rank: 105/108	feyman_priv (contact)	sift-gpu	sift-gpu (128 float32: 512 bytes)	SIFT with 2048 features, using the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC, and setting keypoint orientation to a constant value to increase performance.	N/A	N/A	20-04-25	is_submission, is_challenge_2020
Submission ID: 00609 Key.Net-s-ref + HyNet w/ DEGENSA... Size: 512 bytes. Matches: built-in	2032.93	270.04 Rank: 21/108	0.461 Rank: 2/108	0.820 Rank: 10/108	0.46902 (±0.00032) Rank: 16/108	280.40 Rank: 41/108	1489.62 Rank: 49/108	4.689 Rank: 20/108	0.436 Rank: 25/108	0.68120 (±0.00119) Rank: 25/108	0.57511 Rank: 21/108	Barroso-Laguna, Axel and Tian, Yurun, Ng, Tony and Mikolajczyk, Krystian (contact)	keynet-s-ref	hynet (128 float32: 512 bytes)		N/A	N/A	20-06-01	is_submission, is_challenge_2020
Submission ID: 00033 SOSNet, DEGENSAC Size: 512 bytes. Matches: built-in	1936.28	119.91 Rank: 69/108	0.323 Rank: 84/108	0.769 Rank: 63/108	0.37632 (±0.00056) Rank: 68/108	145.47 Rank: 96/108	1271.41 Rank: 79/108	4.045 Rank: 83/108	0.524 Rank: 73/108	0.56072 (±0.00077) Rank: 75/108	0.46852 Rank: 74/108	Challenge organizers (contact)	sift-def	sosnet (128 float32: 512 bytes)	SOSNet descriptors extracted on SIFT keypoints and DEGENSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1904.05019	https://github.com/yuruntian/SOSNet	20-04-22	is_baseline
Submission ID: 00701 GeoDesc (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	132.69 Rank: 64/108	0.333 Rank: 60/108	0.798 Rank: 32/108	0.41364 (±0.00033) Rank: 49/108	161.05 Rank: 83/108	1287.26 Rank: 76/108	4.239 Rank: 64/108	0.502 Rank: 62/108	0.58373 (±0.00402) Rank: 67/108	0.49868 Rank: 57/108	Challenge organizers (contact)	sift-def	geodesc-upright (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-06-01	is_baseline
Submission ID: 00040 DELF-GLD (128D), DEGENSAC Size: 512 bytes. Matches: built-in	2036.82	100.78 Rank: 91/108	0.109 Rank: 102/108	0.151 Rank: 105/108	0.08367 (±0.00047) Rank: 100/108	430.68 Rank: 13/108	2162.72 Rank: 13/108	2.535 Rank: 104/108	0.880 Rank: 104/108	0.13247 (±0.00116) Rank: 103/108	0.10807 Rank: 103/108	Challenge organizers (contact)	delf-gld-2k-128d	delf-gld-2k-128d (128 float32: 512 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 128 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with DEGENSAC.	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00564 SIFT2k_2000_HardNet64_train_all_... Size: 512 bytes. Matches: built-in	1999.83	156.93 Rank: 44/108	0.336 Rank: 54/108	0.814 Rank: 14/108	0.42959 (±0.00060) Rank: 39/108	244.13 Rank: 52/108	1646.76 Rank: 41/108	4.334 Rank: 45/108	0.459 Rank: 36/108	0.64407 (±0.00339) Rank: 37/108	0.53683 Rank: 39/108	Ximin Zheng, Sheng He, Hualong Shi (contact)	sift2k	sift2k-2000-hardnet64-train-all-l2-138000 (128 float32: 512 bytes)	SIFT with 2000 keypoints(scale 12), hardnet64 with 128 descriptors(trained with l2 loss and step 138000), FLANN disabled	N/A	N/A	20-05-13	is_submission, is_challenge_2020
Submission ID: 00616 Sift-Fusion_Avg-NM-Net_End-to-En... Size: 512 bytes. Matches: custom	1892.70	142.38 Rank: 56/108	0.333 Rank: 76/108	0.801 Rank: 28/108	0.44818 (±0.00000) Rank: 28/108	146.58 Rank: 94/108	1339.45 Rank: 67/108	4.406 Rank: 40/108	0.456 Rank: 33/108	0.65563 (±0.00209) Rank: 34/108	0.55190 Rank: 29/108	Chen Zhao (contact)	siftdef	fusion-avg (128 float32: 512 bytes)	SIFT and Fusion_Avg, followed by NM-Net_End-to-End and DEGENSAC.	N/A	N/A	20-05-31	is_submission, is_challenge_2020
Submission ID: 00003 HardNet (upright), DEGENSAC Size: 512 bytes. Matches: built-in	1892.71	141.66 Rank: 57/108	0.333 Rank: 60/108	0.803 Rank: 27/108	0.44235 (±0.00127) Rank: 31/108	193.09 Rank: 64/108	1435.93 Rank: 56/108	4.302 Rank: 56/108	0.474 Rank: 49/108	0.62275 (±0.00423) Rank: 48/108	0.53255 Rank: 42/108	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and DEGENSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-22	is_baseline
Submission ID: 00030 Upright Root-SIFT (OpenCV), DEGE... Size: 512 bytes. Matches: built-in	1892.72	111.69 Rank: 81/108	0.333 Rank: 56/108	0.781 Rank: 47/108	0.39547 (±0.00027) Rank: 57/108	201.15 Rank: 60/108	1347.75 Rank: 65/108	4.091 Rank: 77/108	0.522 Rank: 72/108	0.56078 (±0.00264) Rank: 74/108	0.47812 Rank: 64/108	Challenge organizers (contact)	sift-def	rootsift-upright (128 float32: 512 bytes)	Upright Root-SIFT with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC, and setting keypoint orientation to a constant value.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00027 DELF-GLD (32D), PyRANSAC Size: 128 bytes. Matches: built-in	2036.82	91.18 Rank: 98/108	0.109 Rank: 102/108	0.122 Rank: 106/108	0.06197 (±0.00032) Rank: 107/108	453.19 Rank: 7/108	1805.48 Rank: 27/108	2.425 Rank: 106/108	0.916 Rank: 107/108	0.09163 (±0.00140) Rank: 106/108	0.07680 Rank: 107/108	Challenge organizers (contact)	delf-gld-2k-32d	delf-gld-2k-32d (32 float32: 128 bytes)	DELF-GLD, with up to 2k features. Descriptors are cropped to 32 dimensions with PCA. Re-using optimal parameters for the (default) 40D models. Stereo with PyRANSAC (DEGENSAC with the degeneracy check turned off).	https://arxiv.org/abs/1812.01584	https://github.com/tensorflow/models/tree/master/research/delf	20-04-22	is_baseline
Submission ID: 00037 GeoDesc, MAGSAC Size: 512 bytes. Matches: built-in	1936.28	115.17 Rank: 78/108	0.323 Rank: 90/108	0.742 Rank: 78/108	0.34024 (±0.00050) Rank: 79/108	120.68 Rank: 104/108	1098.70 Rank: 97/108	4.002 Rank: 89/108	0.551 Rank: 85/108	0.50594 (±0.00073) Rank: 91/108	0.42309 Rank: 85/108	Challenge organizers (contact)	sift-def	geodesc (128 float32: 512 bytes)	GeoDesc descriptors extracted on SIFT keypoints and MAGSAC for stereo. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1807.06294	https://github.com/lzx551402/geodesc	20-04-23	is_baseline
Submission ID: 00586 KeyNet+HardNet+NM-Net_v2 Size: 512 bytes. Matches: custom	2042.89	160.25 Rank: 42/108	0.435 Rank: 18/108	0.821 Rank: 7/108	0.40264 (±0.00000) Rank: 52/108	165.01 Rank: 80/108	1134.69 Rank: 95/108	4.720 Rank: 19/108	0.448 Rank: 30/108	0.67488 (±0.00197) Rank: 27/108	0.53876 Rank: 35/108	Chen Zhao (contact)	keynet	hardnet (128 float32: 512 bytes)	KeyNet and HardNet, followed by NM-Net_v2 and DEGENSAC.	N/A	N/A	20-05-23	is_submission, is_challenge_2020
Submission ID: 00046 D2-Net (single-scale), DEGENSAC Size: 2048 bytes. Matches: built-in	2038.22	147.53 Rank: 54/108	0.202 Rank: 99/108	0.337 Rank: 100/108	0.16324 (±0.00082) Rank: 97/108	364.68 Rank: 21/108	1984.98 Rank: 21/108	3.412 Rank: 98/108	0.720 Rank: 98/108	0.36911 (±0.00167) Rank: 98/108	0.26618 Rank: 97/108	Challenge organizers (contact)	d2net-singlescale	d2net-singlescale (512 float32: 2048 bytes)	D2-Net, single-scale model, up to 2048 features. Trained on the MegaDepth dataset, removing scenes which overlap with the Phototourism test set. Stereo with DEGENSAC and optimal parameters. Parameters tuned separately from the 8k submission.	http://openaccess.thecvf.com/content_CVPR_2019/papers/Dusmanu_D2-Net_A_Trainable_CNN_for_Joint_Description_and_Detection_of_CVPR_2019_paper.pdf	https://github.com/mihaidusmanu/d2-net	20-04-23	is_baseline
Submission ID: 00710 DISK-32D (epi) Size: 128 bytes. Matches: built-in	2048.00	352.43 Rank: 11/108	0.459 Rank: 3/108	0.826 Rank: 5/108	0.45946 (±0.00023) Rank: 22/108	484.30 Rank: 4/108	2324.68 Rank: 5/108	5.589 Rank: 3/108	0.427 Rank: 21/108	0.69938 (±0.00192) Rank: 21/108	0.57942 Rank: 17/108	Michal Tyszkiewicz (contact)	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048	disk-2020-09-15-nms-5-epi-32-save-49-imsize-1024-nump-2048 (32 float32: 128 bytes)	Local feature model learned via policy gradient, using 32D descriptors. Model trained with a cycle-consistency loss and supervised with epipolar constraints. Trained on MegaDepth, removing conflicts with the test data. For inference, images are resized to 1024 pixels on the longest edge, with NMS over a 5x5 window, taking the top 2048 features by score.	N/A	N/A	20-09-16	is_baseline
Submission ID: 00642 MT-2-Hardnet-Pretraind-all-Datas... Size: 512 bytes. Matches: custom	2048.00	140.36 Rank: 58/108	0.450 Rank: 4/108	0.571 Rank: 97/108	0.19708 (±0.00000) Rank: 95/108	146.55 Rank: 95/108	1279.68 Rank: 77/108	4.057 Rank: 79/108	0.542 Rank: 84/108	0.53330 (±0.00263) Rank: 83/108	0.36519 Rank: 93/108	Anonymous (to be released: 2020-6-12)	mt-2	hardnet (128 float32: 512 bytes)	Local feature model learned via training with covariant constraint loss function. We take the top 2048 features by score. HardNet ,pre-trained on all datasets, is used as a descriptor head. Ordered-Aware(OA) Network, trained from scratch on sift-side-8k features (inlier threshold of network output = 1.0, post process method = None, with other settings set to default), is used to compute robust matches and OANet is used in place of robust model estimator.	Anonymous (to be released: 2020-6-12)	Anonymous (to be released: 2020-6-12)	21-03-07	is_submission
Submission ID: 00134 CV-DoG-AffNet-HardNet-kornia-DEG... Size: 512 bytes. Matches: built-in	2047.84	152.12 Rank: 50/108	0.339 Rank: 49/108	0.791 Rank: 37/108	0.41972 (±0.00027) Rank: 45/108	284.06 Rank: 38/108	1788.70 Rank: 30/108	4.191 Rank: 72/108	0.511 Rank: 65/108	0.58536 (±0.00125) Rank: 64/108	0.50254 Rank: 55/108	Challenge organizers (contact)	sift8k	affnethardnet (128 float32: 512 bytes)	OpenCV DoG keypoints, followed by AffNet shape estimation and HardNet descriptor. Implementation: OpenCV + kornia library	https://arxiv.org/abs/1711.06704	https://kornia.readthedocs.io/en/latest/feature.html	21-02-05	is_baseline
Submission ID: 00029 SuperPoint (2k features, NMS=4),... Size: 1024 bytes. Matches: built-in	2048.00	122.69 Rank: 68/108	0.364 Rank: 37/108	0.630 Rank: 91/108	0.28965 (±0.00063) Rank: 87/108	170.60 Rank: 76/108	1185.38 Rank: 90/108	4.331 Rank: 47/108	0.557 Rank: 87/108	0.54663 (±0.00218) Rank: 81/108	0.41814 Rank: 86/108	Challenge organizers (contact)	superpoint-nms4-r1200	superpoint-nms4-r1200 (256 float32: 1024 bytes)	SuperPoint (initial release based on Pytorch). Lowered detection threshold to obtain up to 2k features per image. Using the built-in Non-Maxima Suppression filter at 2 pixels. Images are resized to 1200 pixels on the largest side.	https://arxiv.org/abs/1712.07629	https://github.com/MagicLeapResearch/SuperPointPretrainedNetwork	20-04-22	is_baseline
Submission ID: 00017 ORB (OpenCV), DEGENSAC Size: 32 bytes. Matches: built-in	2031.83	63.43 Rank: 105/108	0.366 Rank: 32/108	0.523 Rank: 99/108	0.08243 (±0.00058) Rank: 101/108	120.93 Rank: 103/108	280.38 Rank: 108/108	2.191 Rank: 108/108	0.863 Rank: 101/108	0.02318 (±0.00229) Rank: 108/108	0.05280 Rank: 108/108	Challenge organizers (contact)	orb	orb (32 uint8: 32 bytes)	ORB with (up to) 2048 features, using the built-in matcher (bidirectional filter with the both strategy) and DEGENSAC.	N/A	https://opencv.org	20-04-22	is_baseline
Submission ID: 00523 SEKD Size: 512 bytes. Matches: built-in	2043.44	116.81 Rank: 74/108	0.386 Rank: 26/108	0.799 Rank: 31/108	0.42811 (±0.00096) Rank: 40/108	166.41 Rank: 79/108	1163.31 Rank: 92/108	4.427 Rank: 37/108	0.456 Rank: 34/108	0.64919 (±0.00171) Rank: 35/108	0.53865 Rank: 36/108	Yafei Song, Ling Cai, Mingyang Li (contact)	sekd	sekd (128 float32: 512 bytes)	Our method named SEKD: Self-Evolving Keypoint Detection and Description, where the SEKD model is trained using COCO test images. In this submission each image has up to 2048 SEKD keypoints, and 128-dim float descriptor. We use the built-in matcher (bidirectional filter with the both strategy, optimal inlier and ratio test thresholds) with DEGENSAC.	N/A	N/A	20-04-29	is_submission, is_challenge_2020
Submission ID: 00651 sp_ae_sg_degensac Size: 512 bytes. Matches: custom	1873.96	288.87 Rank: 19/108	0.323 Rank: 81/108	0.693 Rank: 85/108	0.44506 (±0.00000) Rank: 30/108	295.52 Rank: 33/108	1994.42 Rank: 20/108	4.730 Rank: 17/108	0.395 Rank: 13/108	0.73681 (±0.00306) Rank: 13/108	0.59093 Rank: 15/108	(contact)	superpoint	superpoint-down128 (128 float32: 512 bytes)	SP with 2048 features, and down load.	N/A	N/A	21-04-05	is_submission
Submission ID: 00035 HardNet (upright), MAGSAC Size: 512 bytes. Matches: built-in	1892.71	181.81 Rank: 34/108	0.333 Rank: 60/108	0.781 Rank: 46/108	0.43816 (±0.00058) Rank: 34/108	193.09 Rank: 64/108	1435.93 Rank: 56/108	4.302 Rank: 56/108	0.473 Rank: 48/108	0.62275 (±0.00423) Rank: 48/108	0.53045 Rank: 44/108	Challenge organizers (contact)	sift-def	hardnet-upright (128 float32: 512 bytes)	HardNet descriptors extracted on SIFT keypoints with a fixed orientation, and MAGSAC for stereo. Re-using parameters found for the orientation-sensitive variant. Please refer to the baselines repository (linked) for details.	https://arxiv.org/abs/1705.10872	https://github.com/DagnyT/hardnet	20-04-23	is_baseline