We propose AdaDS, a generalizable framework for depth super-resolution that robustly recovers high-resolution depth maps from arbitrarily degraded low-resolution inputs. Unlike conventional approaches that directly regress depth values and often exhibit artifacts under severe or unknown degradation, AdaDS capitalizes on the contraction property of Gaussian smoothing: as noise accumulates in the forward process, distributional discrepancies between degraded inputs and their pristine high-quality counterparts diminish, ultimately converging to isotropic Gaussian prior. Leveraging this, AdaDS adaptively selects a starting timestep in the reverse diffusion trajectory based on estimated refinement uncertainty, and subsequently injects tailored noise to position the intermediate sample within the high-probability region of the target posterior distribution. This strategy ensures inherent robustness, enabling generative prior of a pre-trained diffusion model to dominate recovery even when upstream estimations are imperfect. Extensive experiments on real-world and synthetic benchmarks demonstrate AdaDS's superior zero-shot generalization and resilience to diverse degradation patterns compared to state-of-the-art methods.

翻译：本文提出AdaDS，一种可泛化的深度超分辨率框架，能够从任意退化的低分辨率输入中鲁棒地恢复高分辨率深度图。与传统直接回归深度值、在严重或未知退化条件下常出现伪影的方法不同，AdaDS利用了高斯平滑的收缩特性：随着前向过程中噪声的累积，退化输入与其原始高质量对应物之间的分布差异逐渐减小，最终收敛至各向同性高斯先验。基于此，AdaDS根据估计的细化不确定性自适应地选择反向扩散轨迹的起始时间步，随后注入定制化噪声，将中间样本定位在目标后验分布的高概率区域内。该策略确保了固有的鲁棒性，即使在上游估计不完美的情况下，也能使预训练扩散模型的生成先验主导恢复过程。在真实世界和合成基准上的大量实验表明，相较于现有先进方法，AdaDS在零样本泛化能力和对多样化退化模式的适应性方面均表现出优越性。