A framework for stereo vision via optimal transport

TL;DR

This paper introduces a novel stereo vision framework using optimal transport, enabling accurate disparity estimation and occlusion handling while improving computational efficiency.

Contribution

It develops a regularized optimal transport-based method for stereo matching, addressing occlusions and enhancing speed and accuracy.

Findings

Accurate disparity functions achieved with the proposed method.

Effective handling of occluded regions in stereo images.

Improved computational speed through regularization.

Abstract

We present a theoretical framework for a stereo vision method via optimal transport tools. We consider two aligned optical systems and we develop the matching between the two pictures line by line. By considering a regularized version of the optimal transport, we can speed up the computation and obtain a very accurate disparity function evaluation. Moreover, via this same method we can approach successfully the case of images with occluded regions.