Depth Anything with Any Prior

• URL: http://arxiv.org/abs/2505.10565v1
• Date: Thu, 15 May 2025 17:59:50 GMT
• Title: Depth Anything with Any Prior
• Authors: Zehan Wang, Siyu Chen, Lihe Yang, Jialei Wang, Ziang Zhang, Hengshuang Zhao, Zhou Zhao,
• Abstract summary: Prior Depth Anything is a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in depth prediction.<n>We develop a conditioned monocular depth estimation (MDE) model to refine the inherent noise of depth priors.<n>Our model showcases impressive zero-shot generalization across depth completion, super-resolution, and inpainting over 7 real-world datasets.
• Score: 64.39991799606146
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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