Year: 2026
Type: preprint
Abstract: We propose AdaDS, an Adaptive Diffusion Sampling framework for prompted metric depth estimation, which estimates high-resolution metric depth from an image and an arbitrarily degraded low-resolution depth prompt. This setting is commonly studied as depth super-resolution, where existing methods typically regress depth directly without an explicit prior over high-quality depth, and thus often produce artifacts under severe or unseen degradations. In contrast, AdaDS exploits the contraction property of Gaussian smoothing: as noise accumulates in the forward diffusion process, the noisy distributions of degraded prompts and their high-quality counterparts converge toward an isotropic Gaussian prior, while the prompt content is gradually erased. Accordingly, AdaDS refines the prompt and estimates its uncertainty to adaptively select a starting timestep, and injects learned noise to place the intermediate sample in a high-density region of the depth distribution learned by a pre-trained diffusion model. Denoising from this sample yields high-quality depth with fine-grained details and corrects residual errors, even under imperfect prompt refinement. Experiments on real-world and synthetic benchmarks demonstrate its superior zero-shot generalization and robustness to diverse degradations.
Source: arXiv (Cornell University)
Authors: Wang, Kun, Zhu, Yun, Zhou, Pan, Zhao, Na
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128617899
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