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

Autor:	Li, Zhaoshuo, Liu, Xingtong, Drenkow, Nathan, Ding, Andy, Creighton, Francis X., Taylor, Russell H., Unberath, Mathias
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Zdroj:	ICCV 2021 Oral
Druh dokumentu:	Working Paper
Popis:	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. Comment: Our code is available at https://github.com/mli0603/stereo-transformer
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.02910 Zobrazit plný text záznamu View this record from Arxiv