Papers › Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency

Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency

19 Sep 2019arXiv:1909.09115archive 2025-07-28

Tianwei Shen, Lei Zhou, Zixin Luo, Yao Yao, Shiwei Li, Jiahui Zhang, Tian Fang, Long Quan

The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data. In this paper, we address the issue when previous assumptions of the self-supervised approaches are violated due to the dynamic nature of real-world scenes. Different from handling the noise as uncertainty, our key idea is to incorporate more robust geometric quantities and enforce internal consistency in the temporal image sequence. As demonstrated on commonly used benchmark datasets, the proposed method substantially improves the state-of-the-art methods on both depth and relative pose estimation for monocular image sequences, without adding inference overhead.

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