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Papers › Unsupervised Scale-consistent Depth Learning from Video

Unsupervised Scale-consistent Depth Learning from Video

25 May 2021arXiv:2105.11610archive 2025-07-28

Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions include: (i) we propose a geometry consistency loss, which penalizes the inconsistency of predicted depths between adjacent views; (ii) we propose a self-discovered mask to automatically localize moving objects that violate the underlying static scene assumption and cause noisy signals during training; (iii) we demonstrate the efficacy of each component with a detailed ablation study and show high-quality depth estimation results in both KITTI and NYUv2 datasets. Moreover, thanks to the capability of scale-consistent prediction, we show that our monocular-trained deep networks are readily integrated into the ORB-SLAM2 system for more robust and accurate tracking. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training. Finally, we provide several demos for qualitative evaluation.

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Code

JiawangBian/sc_depth_pl officialpytorchGPL-3.0 report
JiawangBian/SC-SfMLearner-Release pytorch report

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Tasks

Depth EstimationMonocular Depth EstimationMonocular Visual OdometrySimultaneous Localization and Mapping

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) Delta < 1.25 0.873 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) Delta < 1.25^2 0.960 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) Delta < 1.25^3 0.982 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) RMSE 4.706 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) RMSE log 0.191 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet 50) absolute relative error 0.114 #65 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) Delta < 1.25 0.863 #67 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) Delta < 1.25^2 0.957 #67 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) Delta < 1.25^3 0.981 #67 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) RMSE 4.950 #67 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) RMSE log 0.197 #67 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split SC-Depth (ResNet18) absolute relative error 0.119 #67 of 79 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 self-supervised Bian et al Absolute relative error (AbsRel) 0.157 #6 of 8 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 self-supervised Bian et al Root mean square error (RMSE) 0.593 #6 of 8 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 self-supervised Bian et al delta_1 78.0 #6 of 8 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 self-supervised Bian et al delta_2 94.0 #6 of 8 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 self-supervised Bian et al delta_3 98.4 #6 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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