Papers › Depth Map Decomposition for Monocular Depth Estimation

Depth Map Decomposition for Monocular Depth Estimation

23 Aug 2022arXiv:2208.10762archive 2025-07-28

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

Depth EstimationMonocular Depth Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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