Image Matching Challenge 2021

The 2022 IMC challenge will take place on Kaggle.

Ground truth has been released, please only submit processed results

Local features have played a key role in a wide range of computer vision applications throughout the past 20 years. Despite the drastic advancements in many fields after the deep learning revolution, end-to-end solutions for 3D reconstruction under challenging conditions remain elusive, and solutions based on traditional, hand-crafted methods such as SIFT (1999) and RANSAC (1981) may outperform the state of the art in machine learning.

Historically, local features have been evaluated on small datasets using proxy metrics, such as patch retrieval accuracy for descriptors. This may not necessarily translate to downstream applications, misrepresenting their actual performance and hindering the development of new techniques. There is a clear need for large-scale, challenging benchmarks to both train and evaluate new strategies for image matching. Towards this end, we held in 2019 an open challenge in image matching across wide baselines. In contrast to existing benchmarks, we only consider integrated solutions and our primary metric measures the accuracy of the reconstructed poses — the final goal.

We are pleased to introduce the third edition of this challenge. It is co-located with the CVPR 2021 workshop on Image Matching: Local Features and Beyond. Winners and selected participants will be invited to give talks at the conference, as last year. Prizes are available, thanks to our sponsors.


Key Dates

2021 Challenge Highlights


Eduard Trulls Google
Yuhe Jin UBC
Kwang Yi UBC
Dmytro Mishkin CTU Prague
Jiri Matas CTU Prague