CFDNet：一种基于对比特征蒸馏的可泛雾化雾天立体匹配网络
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation
AI总结：
针对雾天立体匹配难度高、单纯去雾反会损失深度线索的问题，提出基于对比特征蒸馏的CFDNet框架，平衡雾天深度线索与清洁匹配特征的依赖，提升跨场景泛化性，实验验证了其性能优势。
摘要：
雾天场景下的立体匹配仍是一项具有挑战性的任务，因为散射效应会降低能见度，导致用于稠密对应匹配的特征区分度更低。此前一些基于学习的方法整合了物理散射函数，以同时实现立体匹配和去雾，但单纯去除雾可能无助于深度估计，因为雾本身能提供关键的深度线索。在本研究中，我们提出了一种基于对比特征蒸馏（CFD）的框架。该策略将融合的清洁-雾天特征的特征蒸馏与对比学习相结合，确保对雾天深度线索和清洁匹配特征的依赖达到平衡，有助于提升模型在清洁与雾天环境下的泛化能力。在合成数据集和真实世界数据集上开展的综合实验证实了我们方法的优异性能与适应性。
英文摘要：
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.
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