  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.
