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.

翻译：当前最先进的单目深度估计模型在挑战性环境中往往表现不佳，主要原因是其忽略了鲁棒的物理信息。为验证此观点，我们首先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强而增加。基于此发现，我们提出PhysDepth——一种即插即用框架，通过向现代最优主干网络注入物理先验来解决这一脆弱性问题。PhysDepth包含两个核心组件：利用瑞利散射理论从高信噪比红色通道提取鲁棒特征的物理先验模块，以及通过物理推导的红色通道衰减损失函数来强制模型学习比尔-朗伯定律。大量实验评估表明，PhysDepth在挑战性条件下达到了最优精度。