Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching. While some previous learning-based methods integrated a physical scattering function for simultaneous stereo-matching and dehazing, simply removing fog might not aid depth estimation because the fog itself can provide crucial depth cues. In this work, we introduce a framework based on contrastive feature distillation (CFD). This strategy combines feature distillation from merged clean-fog features with contrastive learning, ensuring balanced dependence on fog depth hints and clean matching features. This framework helps to enhance model generalization across both clean and foggy environments. Comprehensive experiments on synthetic and real-world datasets affirm the superior strength and adaptability of our method.

在模糊场景下，立体匹配因散射效应导致能见度下降和密集对应匹配特征较不明显，而简单地去除雾可能无助于深度估计，因为雾本身可以提供重要的深度线索。本文介绍了一种基于对比特征蒸馏的框架，该策略结合了从合并的清晰-雾特征中蒸馏特征和对比学习，确保对雾深度线索和清晰匹配特征的均衡依赖，提高了模型在清晰和模糊环境下的推广能力。对合成和真实数据集的全面实验证实了我们方法的优越性和适应性。

CFDNet：一种具有对比特征蒸馏的可概化的雾视觉立体匹配网络