Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. This approach, named STereo TRansformer (STTR), has several advantages: It 1) relaxes the limitation of a fixed disparity range, 2) identifies occluded regions and provides confidence estimates, and 3) imposes uniqueness constraints during the matching process. We report promising results on both synthetic and real-world datasets and demonstrate that STTR generalizes across different domains, even without fine-tuning.
@article{arxiv.2011.02910,
title = {Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers},
author = {Zhaoshuo Li and Xingtong Liu and Nathan Drenkow and Andy Ding and Francis X. Creighton and Russell H. Taylor and Mathias Unberath},
journal= {arXiv preprint arXiv:2011.02910},
year = {2021}
}
Comments
Our code is available at https://github.com/mli0603/stereo-transformer