Metric Depth Estimation from Arbitrarily Degraded Low-Resolution Depth Prompts

We propose AdaDS, a generalizable framework for prompted metric depth estimation, which estimates high-resolution metric depth from images and arbitrarily degraded low-resolution depth prompts. This setting is commonly studied as depth super-resolution, where existing methods typically regress depth values directly and often exhibit artifacts under severe or unknown depth degradation. In contrast, AdaDS exploits the contraction property of Gaussian smoothing: as noise accumulates in the forward diffusion process, the distributional discrepancy between degraded depth prompts and their high-quality counterparts progressively diminishes, eventually approaching an isotropic Gaussian prior. Leveraging this property, AdaDS estimates refinement uncertainty to adaptively select a starting timestep in the reverse diffusion trajectory, and subsequently injects tailored noise to place the intermediate sample in a high-probability region of the target posterior distribution. This strategy enables the generative prior of a pre-trained diffusion model to dominate the estimation process even when upstream prompt refinements are imperfect. Extensive experiments on real-world and synthetic benchmarks demonstrate AdaDS's superior zero-shot generalization and robustness to diverse degradation patterns compared with state-of-the-art methods.