title: Depth Anything with Any Prior
summary: This work presents Prior Depth Anything, a framework that combines incomplete
  but precise metric information in depth measurement with relative but complete
  geometric structures in depth prediction, generating accurate, dense, and
  detailed metric depth maps for any scene. To this end, we design a
  coarse-to-fine pipeline to progressively integrate the two complementary depth
  sources. First, we introduce pixel-level metric alignment and distance-aware
  weighting to pre-fill diverse metric priors by explicitly using depth
  prediction. It effectively narrows the domain gap between prior patterns,
  enhancing generalization across varying scenarios. Second, we develop a
  conditioned monocular depth estimation (MDE) model to refine the inherent noise
  of depth priors. By conditioning on the normalized pre-filled prior and
  prediction, the model further implicitly merges the two complementary depth
  sources. Our model showcases impressive zero-shot generalization across depth
  completion, super-resolution, and inpainting over 7 real-world datasets,
  matching or even surpassing previous task-specific methods. More importantly,
  it performs well on challenging, unseen mixed priors and enables test-time
  improvements by switching prediction models, providing a flexible
  accuracy-efficiency trade-off while evolving with advancements in MDE models.
paper_authors._id: 6826bda8caf89edf94b736dc
paper_authors.name: Zehan Wang
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paper_authors.fullname: zehan wang
paper_authors.type: user
paper_authors.user: sleetwang6
paper_authors._id: 6826bda8caf89edf94b736dd
paper_authors.name: Siyu Chen
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paper_authors.fullname: Siyu Chen
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paper_authors.user: SiyuChen
paper_authors._id: 6826bda8caf89edf94b736de
paper_authors.name: Lihe Yang
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paper_authors.fullname: Lihe Yang
paper_authors.type: user
paper_authors.user: LiheYoung
paper_authors._id: 6826bda8caf89edf94b736df
paper_authors.name: Jialei Wang
paper_authors.status: admin_assigned
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paper_authors._id: 661b66368ad066992421a8c9
paper_authors.avatarUrl: /avatars/6d970c07b7366563fd7a73d48ac496ae.svg
paper_authors.fullname: Jialei Wang
paper_authors.type: user
paper_authors.user: UncleWang233
paper_authors._id: 6826bda8caf89edf94b736e0
paper_authors.name: Ziang Zhang
paper_authors._id: 6826bda8caf89edf94b736e1
paper_authors.name: Hengshuang Zhao
paper_authors._id: 6826bda8caf89edf94b736e2
paper_authors.name: Zhou Zhao
paper_title: Depth Anything with Any Prior
paper_summary: This work presents Prior Depth Anything, a framework that combines incomplete
  but precise metric information in depth measurement with relative but complete
  geometric structures in depth prediction, generating accurate, dense, and
  detailed metric depth maps for any scene. To this end, we design a
  coarse-to-fine pipeline to progressively integrate the two complementary depth
  sources. First, we introduce pixel-level metric alignment and distance-aware
  weighting to pre-fill diverse metric priors by explicitly using depth
  prediction. It effectively narrows the domain gap between prior patterns,
  enhancing generalization across varying scenarios. Second, we develop a
  conditioned monocular depth estimation (MDE) model to refine the inherent noise
  of depth priors. By conditioning on the normalized pre-filled prior and
  prediction, the model further implicitly merges the two complementary depth
  sources. Our model showcases impressive zero-shot generalization across depth
  completion, super-resolution, and inpainting over 7 real-world datasets,
  matching or even surpassing previous task-specific methods. More importantly,
  it performs well on challenging, unseen mixed priors and enables test-time
  improvements by switching prediction models, providing a flexible
  accuracy-efficiency trade-off while evolving with advancements in MDE models.
paper_ai_summary: Prior Depth Anything framework integrates metric depth measurement and geometric depth prediction into a coarse-to-fine pipeline for accurate, dense, and detailed depth maps, demonstrating superior zero-shot performance across multiple tasks.
paper_ai_keywords: metric alignment distance-aware weighting conditioned monocular depth estimation MDE depth prior refinement domain gap zero-shot generalization depth completion super-resolution depth inpainting mixed priors test-time improvements