Temporally coherent stereo: improving performance through knowledge of motion

This paper introduces the idea of temporally extending results of a stereo algorithm in order to improve the algorithm's performance. This approach anticipates the changes between two consecutive depth maps resulting from the motion of the cameras. Uncertainties in motion are accounted for by computation of an ambiguity area and a resulting disparity range for each pixel. The computation is used to verify and refine the anticipated values, rather than calculate them without prior knowledge. The paper compares the performance of the algorithm under different constraints on motion. Speedups of up to 400% are achieved without significant errors.