  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.
