CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the vis- ibility and results in less distinctive features for dense cor- respondence matching. While some previous learning-based methods integrated a physical scattering function for simultane- ous 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 adapt- ability of our method.