Papers › Deeper into Self-Supervised Monocular Indoor Depth Estimation
Deeper into Self-Supervised Monocular Indoor Depth Estimation
Chao Fan, Zhenyu Yin, Yue Li, Feiqing Zhang
Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challenging for researchers because of the following two main reasons. One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets. In this work, our proposed method, named IndoorDepth, consists of two innovations. In particular, we first propose a novel photometric loss with improved structural similarity (SSIM) function to tackle the challenge from low-texture regions. Moreover, in order to further mitigate the issue of inaccurate ego-motion prediction, multiple photometric losses at different stages are used to train a deeper pose network with two residual pose blocks. Subsequent ablation study can validate the effectiveness of each new idea. Experiments on the NYUv2 benchmark demonstrate that our IndoorDepth outperforms the previous state-of-the-art methods by a large margin. In addition, we also validate the generalization ability of our method on ScanNet dataset. Code is availabe at https://github.com/fcntes/IndoorDepth.
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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 | IndoorDepth | Absolute relative error (AbsRel) | 0.126 | #1 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | IndoorDepth | Root mean square error (RMSE) | 0.494 | #1 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | IndoorDepth | delta_1 | 84.5 | #1 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | IndoorDepth | delta_2 | 96.5 | #1 of 8 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 self-supervised | IndoorDepth | delta_3 | 99.1 | #1 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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