当前位置:网站首页>用变压器从序列到序列的角度重新进行立体深度估计(CS CV)

用变压器从序列到序列的角度重新进行立体深度估计(CS CV)

2020-12-07 19:16:43 凌茜

立体深度估计依赖于左右图像中极线上像素点之间的最佳对应匹配来推断深度。在这项工作中,我们不再匹配单个像素,而是从序列到序列对应的角度来重新讨论这个问题,以使用位置信息和注意力的密集像素匹配代替代价体积构造。这种方法(STTR)具有以下优点:1)放宽了固定视差范围的限制;2)识别遮挡区域并提供估计置信度;3)在匹配过程中施加唯一性约束。我们在合成数据集和真实世界数据集上都报告了有希望的结果,并证明了STTR在不同领域的推广效果很好,即使没有微调。我们的代码可以在this https URL中公开获得。

原文标题:Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

原文:Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right image to infer depth. Rather than matching individual pixels, 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 of estimation, 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 well across different domains, even without fine-tuning. Our code is publicly available at this https URL.

原文作者:Zhaoshuo Li, Xingtong Liu, Francis X. Creighton, Russell H. Taylor, Mathias Unberath

原文地址:https://arxiv.org/abs/2011.02910

原创声明,本文系作者授权云+社区发表,未经许可,不得转载。

如有侵权,请联系 yunjia_community@tencent.com 删除。

版权声明
本文为[凌茜]所创,转载请带上原文链接,感谢
https://cloud.tencent.com/developer/article/1747128