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- W3203576757 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." @default.
- W3203576757 created "2021-10-11" @default.
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- W3203576757 date "2020-11-05" @default.
- W3203576757 modified "2023-09-23" @default.
- W3203576757 title "Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers" @default.
- W3203576757 doi "https://doi.org/10.48550/arxiv.2011.02910" @default.
- W3203576757 hasPublicationYear "2020" @default.
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