title: Depth Anything V2
summary: This work presents Depth Anything V2. Without pursuing fancy techniques, we
  aim to reveal crucial findings to pave the way towards building a powerful
  monocular depth estimation model. Notably, compared with V1, this version
  produces much finer and more robust depth predictions through three key
  practices: 1) replacing all labeled real images with synthetic images, 2)
  scaling up the capacity of our teacher model, and 3) teaching student models
  via the bridge of large-scale pseudo-labeled real images. Compared with the
  latest models built on Stable Diffusion, our models are significantly more
  efficient (more than 10x faster) and more accurate. We offer models of
  different scales (ranging from 25M to 1.3B params) to support extensive
  scenarios. Benefiting from their strong generalization capability, we fine-tune
  them with metric depth labels to obtain our metric depth models. In addition to
  our models, considering the limited diversity and frequent noise in current
  test sets, we construct a versatile evaluation benchmark with precise
  annotations and diverse scenes to facilitate future research.
paper_authors._id: 666b9c7580a130166dbd9efc
paper_authors.name: Lihe Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-14T07:23:21.471Z
paper_authors._id: 65a3a0342548c41ad9f4e4e7
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paper_authors.fullname: Lihe Yang
paper_authors.type: user
paper_authors.user: LiheYoung
paper_authors._id: 666b9c7580a130166dbd9efd
paper_authors.name: Bingyi Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-14T07:23:30.766Z
paper_authors._id: 647b5fef6a79fbf5e996c47c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647b5fef6a79fbf5e996c47c/IkSMnDsCY_CyEFCiMDuxe.jpeg
paper_authors.fullname: Bingyi Kang
paper_authors.type: user
paper_authors.user: bykang
paper_authors._id: 666b9c7580a130166dbd9efe
paper_authors.name: Zilong Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-14T07:38:03.094Z
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: 666b9c7580a130166dbd9eff
paper_authors.name: Zhen Zhao
paper_authors._id: 666b9c7580a130166dbd9f00
paper_authors.name: Xiaogang Xu
paper_authors._id: 666b9c7580a130166dbd9f01
paper_authors.name: Jiashi Feng
paper_authors._id: 666b9c7580a130166dbd9f02
paper_authors.name: Hengshuang Zhao
paper_title: Depth Anything V2
paper_summary: This work presents Depth Anything V2. Without pursuing fancy techniques, we
  aim to reveal crucial findings to pave the way towards building a powerful
  monocular depth estimation model. Notably, compared with V1, this version
  produces much finer and more robust depth predictions through three key
  practices: 1) replacing all labeled real images with synthetic images, 2)
  scaling up the capacity of our teacher model, and 3) teaching student models
  via the bridge of large-scale pseudo-labeled real images. Compared with the
  latest models built on Stable Diffusion, our models are significantly more
  efficient (more than 10x faster) and more accurate. We offer models of
  different scales (ranging from 25M to 1.3B params) to support extensive
  scenarios. Benefiting from their strong generalization capability, we fine-tune
  them with metric depth labels to obtain our metric depth models. In addition to
  our models, considering the limited diversity and frequent noise in current
  test sets, we construct a versatile evaluation benchmark with precise
  annotations and diverse scenes to facilitate future research.
paper_ai_summary: Depth Anything V2 improves monocular depth estimation through synthetic images, larger teacher models, and pseudo-labeled real images, achieving better efficiency and accuracy than Stable Diffusion models.
paper_ai_keywords: monocular depth estimation synthetic images teacher model student models pseudo-labeled real images Stable Diffusion efficient accurate metric depth models evaluation benchmark precise annotations diverse scenes