Papers › Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments
Junsheng Zhou, Yuwang Wang, Kaihuai Qin, Wen-Jun Zeng
Recently unsupervised learning of depth from videos has made remarkable progress and the results are comparable to fully supervised methods in outdoor scenes like KITTI. However, there still exist great challenges when directly applying this technology in indoor environments, e.g., large areas of non-texture regions like white wall, more complex ego-motion of handheld camera, transparent glasses and shiny objects. To overcome these problems, we propose a new optical-flow based training paradigm which reduces the difficulty of unsupervised learning by providing a clearer training target and handles the non-texture regions. Our experimental evaluation demonstrates that the result of our method is comparable to fully supervised methods on the NYU Depth V2 benchmark. To the best of our knowledge, this is the first quantitative result of purely unsupervised learning method reported on indoor datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | Zhou et al | Absolute relative error (AbsRel) | 0.208 | #8 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | Zhou et al | Root mean square error (RMSE) | 0.712 | #8 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | Zhou et al | delta_1 | 67.4 | #8 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | Zhou et al | delta_2 | 90.0 | #8 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | Zhou et al | delta_3 | 96.8 | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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