Depth Anything with Any Prior
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.
6826bda8caf89edf94b736dc
Zehan Wang
claimed_verified
2025-05-16T07:12:01.498Z
6425761a175bd295228311a0
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zehan wang
user
sleetwang6
6826bda8caf89edf94b736dd
Siyu Chen
admin_assigned
2025-05-16T14:10:23.599Z
6728865953709f441b66d901
/avatars/5f06b4e335357a77fa702f00a4306124.svg
Siyu Chen
user
SiyuChen
6826bda8caf89edf94b736de
Lihe Yang
admin_assigned
2025-05-16T14:10:32.115Z
65a3a0342548c41ad9f4e4e7
https://cdn-avatars.huggingface.co/v1/production/uploads/65a3a0342548c41ad9f4e4e7/80Yod9z7O95nC-Y0o-5UI.jpeg
Lihe Yang
user
LiheYoung
6826bda8caf89edf94b736df
Jialei Wang
admin_assigned
2025-05-16T14:10:39.079Z
661b66368ad066992421a8c9
/avatars/6d970c07b7366563fd7a73d48ac496ae.svg
Jialei Wang
user
UncleWang233
6826bda8caf89edf94b736e0
Ziang Zhang
6826bda8caf89edf94b736e1
Hengshuang Zhao
6826bda8caf89edf94b736e2
Zhou Zhao
Depth Anything with Any Prior
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.
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.
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