title: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
summary: 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.
paper_authors._id: 65ade82bba78ec6b89e24c75
paper_authors.name: Lihe Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-22T11:26:47.600Z
paper_authors._id: 65a3a0342548c41ad9f4e4e7
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paper_authors.fullname: Lihe Yang
paper_authors.type: user
paper_authors.user: LiheYoung
paper_authors._id: 65ade82bba78ec6b89e24c76
paper_authors.name: Bingyi Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-22T10:28:44.292Z
paper_authors._id: 647b5fef6a79fbf5e996c47c
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paper_authors.fullname: Bingyi Kang
paper_authors.type: user
paper_authors.user: bykang
paper_authors._id: 65ade82bba78ec6b89e24c77
paper_authors.name: Zilong Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-22T11:27:53.983Z
paper_authors._id: 63a430ec84a6a25c65bff20c
paper_authors.avatarUrl: /avatars/3ab51214c491f28d8fe6b70c373d72aa.svg
paper_authors.fullname: Zilong Huang
paper_authors.type: user
paper_authors.user: speedinghzl
paper_authors._id: 65ade82bba78ec6b89e24c78
paper_authors.name: Xiaogang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-22T11:28:00.440Z
paper_authors._id: 637ee45b2438d7485b8d8f6a
paper_authors.avatarUrl: /avatars/11b7d29b6fa6c1b392641e0cd4002863.svg
paper_authors.fullname: Xiaogang Xu
paper_authors.type: user
paper_authors.user: xiaogang00
paper_authors._id: 65ade82bba78ec6b89e24c79
paper_authors.name: Jiashi Feng
paper_authors._id: 65ade82bba78ec6b89e24c7a
paper_authors.name: Hengshuang Zhao
paper_title: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
paper_summary: 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.
paper_ai_keywords: monocular depth estimation foundation model data engine unlabeled data generalization error data augmentation tools robust representations auxiliary supervision pre-trained encoders zero-shot capabilities public datasets metric depth information NYUv2 KITTI new SOTAs depth-conditioned ControlNet