Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
Abstract
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.
Cite
Text
Shen et al. "Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00499Markdown
[Shen et al. "Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/shen2019iccvw-selfsupervised/) doi:10.1109/ICCVW.2019.00499BibTeX
@inproceedings{shen2019iccvw-selfsupervised,
title = {{Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency}},
author = {Shen, Tianwei and Zhou, Lei and Luo, Zixin and Yao, Yao and Li, Shiwei and Zhang, Jiahui and Fang, Tian and Quan, Long},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {4044-4053},
doi = {10.1109/ICCVW.2019.00499},
url = {https://mlanthology.org/iccvw/2019/shen2019iccvw-selfsupervised/}
}