用物理规律提升单目深度估计在复杂环境下的鲁棒性
PhysDepth: Plug-and-Play Physical Refinement for Monocular Depth Estimation in Challenging Environments
- 引入瑞利散射理论提取高信噪比红通道特征
- 通过物理衰减损失使模型学习贝-朗伯定律
- 适合作为现有深度模型的即插即用增强模块
当前最先进的单目深度估计（MDE）模型在复杂环境中表现不佳，主要因忽略了可靠的物理信息。我们通过计算模型预测误差与大气衰减之间的协方差进行实证研究，发现现有最优模型的误差随大气衰减增加而上升。基于此，提出PhysDepth框架，通过注入物理先验来解决这一脆弱性。该框架包含两个关键组件：物理先验模块（PPM），利用瑞利散射理论从高信噪比红通道提取稳健特征；以及基于物理的红通道衰减损失（RCA），强制模型学习贝-朗伯定律。大量实验表明，PhysDepth在挑战性条件下实现了最先进精度。
原文摘要 · Abstract (English)
State-of-the-art monocular depth estimation (MDE) models often struggle in challenging environments, primarily because they overlook robust physical information. To demonstrate this, we first conduct an empirical study by computing the covariance between a model's prediction error and atmospheric attenuation. We find that the error of existing SOTAs increases with atmospheric attenuation. Based on this finding, we propose PhysDepth, a plug-and-play framework that solves this fragility by infusing physical priors into modern SOTA backbones. PhysDepth incorporates two key components: a Physical Prior Module (PPM) that leverages Rayleigh Scattering theory to extract robust features from the high-SNR red channel, and a physics-derived Red Channel Attenuation Loss (RCA) that enforces model to learn the Beer-Lambert law. Extensive evaluations demonstrate that PhysDepth achieves SOTA accuracy in challenging conditions.
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