Papers › Depth Map Decomposition for Monocular Depth Estimation
Depth Map Decomposition for Monocular Depth Estimation
Jinyoung Jun, Jae-Han Lee, Chul Lee, Chang-Su Kim
We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features. The proposed network is composed of a shared encoder and three decoders, called G-Net, N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a metric depth map, respectively. M-Net learns to estimate metric depths more accurately using relative depth features extracted by G-Net and N-Net. The proposed algorithm has the advantage that it can use datasets without metric depth labels to improve the performance of metric depth estimation. Experimental results on various datasets demonstrate that the proposed algorithm not only provides competitive performance to state-of-the-art algorithms but also yields acceptable results even when only a small amount of metric depth data is available for its training.
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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 | Depth-Map-Decomposition-HRWSI | Delta < 1.25 | 0.913 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition-HRWSI | Delta < 1.25^2 | 0.987 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition-HRWSI | Delta < 1.25^3 | 0.998 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition-HRWSI | RMSE | 0.355 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition-HRWSI | absolute relative error | 0.098 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition-HRWSI | log 10 | 0.042 | #48 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | Delta < 1.25 | 0.907 | #50 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | Delta < 1.25^2 | 0.986 | #50 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | Delta < 1.25^3 | 0.997 | #50 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | RMSE | 0.362 | #50 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | absolute relative error | 0.100 | #50 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | Depth-Map-Decomposition | log 10 | 0.043 | #50 of 85 | 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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