Web: http://arxiv.org/abs/2209.06351

Sept. 16, 2022, 1:15 a.m. | Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

cs.CV updates on arXiv.org arxiv.org

Self-supervised depth learning from monocular images normally relies on the
2D pixel-wise photometric relation between temporally adjacent image frames.
However, they neither fully exploit the 3D point-wise geometric
correspondences, nor effectively tackle the ambiguities in the photometric
warping caused by occlusions or illumination inconsistency. To address these
problems, this work proposes Density Volume Construction Network (DevNet), a
novel self-supervised monocular depth learning framework, that can consider 3D
spatial information, and exploit stronger geometric constraints among adjacent
camera frustums. Instead of …

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