Papers › Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

19 Jan 2024CVPR 2024 1arXiv:2401.10891archive 2025-07-28

Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao

This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error. We investigate two simple yet effective strategies that make data scaling-up promising. First, a more challenging optimization target is created by leveraging data augmentation tools. It compels the model to actively seek extra visual knowledge and acquire robust representations. Second, an auxiliary supervision is developed to enforce the model to inherit rich semantic priors from pre-trained encoders. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos. It demonstrates impressive generalization ability. Further, through fine-tuning it with metric depth information from NYUv2 and KITTI, new SOTAs are set. Our better depth model also results in a better depth-conditioned ControlNet. Our models are released at https://github.com/LiheYoung/Depth-Anything.

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LiheYoung/Depth-Anything officialmentioned in papermentioned on GitHubpytorch report
JTRNEO/SynRS3D mentioned on GitHubpytorch report
duan-song/SATNet mentioned on GitHubpytorch report
fabio-sim/Depth-Anything-ONNX mentioned on GitHubpytorchApache-2.0 report
greatenanoymous/monodpt_grasp mentioned on GitHubpytorchApache-2.0 report

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1ran · our draft was wrong
1ran · fixture could not drive it
1ran
8unverified

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load_ckpt LiheYoung/Depth-Anything/metric_depth/train_mono.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · 6ff08048a8305588 · report
apply_min_size spacewalk01/depth-anything-tensorrt/python/depth_anything/util/transform.py community (archive-listed) ran MIT (permissive) · 8d4e5ab503856d09 · report
infer greatenanoymous/monodpt_grasp/metric_depth/evaluate.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 8ffddb7d6410c4f1 · report
depth2PointCloud greatenanoymous/monodpt_grasp/metric_depth/create_pc.py community (archive-listed) unverified Apache-2.0 (permissive) · 8f37f1dad72ee59a · report
depth2PointCloudFakeDepth greatenanoymous/monodpt_grasp/metric_depth/create_pc.py community (archive-listed) unverified Apache-2.0 (permissive) · 0813761694382465 · report
infer_complete_depth greatenanoymous/monodpt_grasp/metric_depth/evaluate_complete.py community (archive-listed) unverified Apache-2.0 (permissive) · 03b9e7ce46a39fe4 · report
infer_relative_depth greatenanoymous/monodpt_grasp/metric_depth/evaluate_relative.py community (archive-listed) unverified Apache-2.0 (permissive) · c7ed25a9b6ac1ab2 · report
load_image spacewalk01/depth-anything-tensorrt/python/depth_anything/util/transform.py community (archive-listed) unverified MIT (permissive) · 24a768b877a487a4 · report
project_point greatenanoymous/monodpt_grasp/metric_depth/camera.py community (archive-listed) unverified Apache-2.0 (permissive) · fe823f83592f9fc3 · report
project_point_to_pixel greatenanoymous/monodpt_grasp/metric_depth/camera.py community (archive-listed) unverified Apache-2.0 (permissive) · 822210307d6c6191 · report
reprojection_error2d greatenanoymous/monodpt_grasp/metric_depth/camera.py community (archive-listed) unverified Apache-2.0 (permissive) · 55658559d5046929 · report

Tasks

Data AugmentationDepth EstimationMonocular Depth EstimationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Monocular Depth Estimation ETH3D Depth Anything Delta < 1.25 0.882 #4 of 10 Archive leaderboard report
Monocular Depth Estimation ETH3D Depth Anything absolute relative error 0.0127 #4 of 10 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything Delta < 1.25 0.982 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything Delta < 1.25^2 0.998 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything Delta < 1.25^3 1.000 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything RMSE 1.896 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything RMSE log 0.069 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything Sq Rel 0.121 #10 of 79 Archive leaderboard report
Monocular Depth Estimation KITTI Eigen split Depth Anything absolute relative error 0.046 #10 of 79 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything Delta < 1.25 0.984 #11 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything Delta < 1.25^2 0.998 #11 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything Delta < 1.25^3 1.000 #11 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything RMSE 0.206 #11 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything absolute relative error 0.056 #11 of 85 Archive leaderboard report
Monocular Depth Estimation NYU-Depth V2 Depth Anything log 10 0.024 #11 of 85 Archive leaderboard report
Semantic Segmentation Cityscapes test Depth Anything Mean IoU (class) 84.8% #8 of 105 Archive leaderboard report
Semantic Segmentation Cityscapes val Depth Anything mIoU 86.2 #8 of 99 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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