Papers › CutDepth:Edge-aware Data Augmentation in Depth Estimation
CutDepth:Edge-aware Data Augmentation in Depth Estimation
Yasunori Ishii, Takayoshi Yamashita
It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths. Data augmentation is thus important to this task. However, there has been little research on data augmentation for tasks such as monocular depth estimation, where the transformation is performed pixel by pixel. In this paper, we propose a data augmentation method, called CutDepth. In CutDepth, part of the depth is pasted onto an input image during training. The method extends variations data without destroying edge features. Experiments objectively and subjectively show that the proposed method outperforms conventional methods of data augmentation. The estimation accuracy is improved with CutDepth even though there are few training data at long distances.
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Code
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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 | CutDepth | Delta < 1.25 | 0.899 | #54 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | CutDepth | Delta < 1.25^2 | 0.985 | #54 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | CutDepth | Delta < 1.25^3 | 0.997 | #54 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | CutDepth | RMSE | 0.375 | #54 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | CutDepth | absolute relative error | 0.104 | #54 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | CutDepth | log 10 | 0.044 | #54 of 85 | Archive leaderboard | report |
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