通过自适应采样实现对任意退化深度图的鲁棒超分辨率重建
Robust Depth Super-Resolution via Adaptive Diffusion Sampling
- 基于高斯平滑的分布收缩特性，自适应选择反向扩散起始步数
- 在真实与合成数据集上均优于现有方法，零样本泛化能力强
- 适合处理未知或严重退化的深度图恢复任务
我们提出AdaDS，一种可泛化的深度超分辨率框架，能从任意退化程度的低分辨率输入中稳健恢复高分辨率深度图。不同于传统直接回归深度值的方法在严重或未知退化下易出现伪影，AdaDS利用高斯平滑的收缩性质：随着前向过程噪声累积，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的高斯先验。基于此，AdaDS根据估计的修复不确定性自适应选择反向扩散轨迹的起始时间步，并注入定制化噪声，将中间样本置于目标后验分布的高概率区域。该策略确保内在鲁棒性，使预训练扩散模型的生成先验即使在上游估计不准确时仍主导恢复过程。在真实世界与合成基准上的大量实验表明，相比当前最优方法，AdaDS展现出更优的零样本泛化能力与对多样化退化模式的韧性。
原文摘要 · Abstract (English)
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.
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