Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers

Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X. Creighton, Russell H. Taylor, Mathias Unberath; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6197-6206

Abstract

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.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Li_2021_ICCV, author = {Li, Zhaoshuo and Liu, Xingtong and Drenkow, Nathan and Ding, Andy and Creighton, Francis X. and Taylor, Russell H. and Unberath, Mathias}, title = {Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {6197-6206} }