Abstract
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.
Abstract (translated)
我们提出了AdaDS，这是一种通用的深度超分辨率框架，能够从任意退化的低分辨率输入中稳健地恢复出高分辨率的深度图。与传统的直接回归深度值的方法不同，在严重或未知退化情况下经常出现伪影，AdaDS 利用了高斯平滑的收缩特性：随着噪声在前向过程中积累，退化输入与其原始高质量版本之间的分布差异逐渐减小，并最终收敛于各向同性的高斯先验。 借助这一点，AdaDS 根据估计的细化不确定性自适应地选择反向扩散轨迹中的起始时间步长，并随后注入定制化的噪声以将中间样本置于目标后验分布的高概率区域内。这种策略确保了固有的鲁棒性，使得预训练扩散模型的生成先验能够主导恢复过程，即使上游估计不完美也是如此。 在现实世界和合成基准测试上的广泛实验表明，与最先进的方法相比，AdaDS 在零样本泛化和对各种退化模式的韧性方面表现更佳。
URL
https://arxiv.org/abs/2602.09510
