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

arXiv:2402.18181v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cv distillation feature features function network results type visibility