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.