Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in the last decade thanks to deep learning. While previous surveys in the late 2010s covered the first stage of this revolution, the last five years of research brought further ground-breaking advancements to the field. This paper aims to fill this gap in a two-fold manner: first, we offer an in-depth examination of the latest developments in deep stereo matching, focusing on the pioneering architectural designs and groundbreaking paradigms that have redefined the field in the 2020s; second, we present a thorough analysis of the critical challenges that have emerged alongside these advances, providing a comprehensive taxonomy of these issues and exploring the state-of-the-art techniques proposed to address them. By reviewing both the architectural innovations and the key challenges, we offer a holistic view of deep stereo matching and highlight the specific areas that require further investigation. To accompany this survey, we maintain a regularly updated project page that catalogs papers on deep stereo matching in our Awesome-Deep-Stereo-Matching (https://github.com/fabiotosi92/Awesome-Deep-Stereo-Matching) repository.

翻译：立体匹配技术已历经近半个世纪的发展，得益于深度学习的推动，其在过去十年中经历了快速演进。尽管2010年代末期的先前综述已涵盖了这场革命的第一阶段，但过去五年的研究为该领域带来了更多突破性进展。本文旨在通过双重方式填补这一空白：首先，我们对深度立体匹配的最新进展进行深入剖析，重点关注2020年代重新定义该领域的开创性架构设计与范式突破；其次，我们对伴随这些进展出现的关键挑战进行全面分析，构建了这些问题的系统分类体系，并探讨了应对这些挑战的最前沿技术。通过梳理架构创新与核心挑战，我们呈现了深度立体匹配的整体图景，并指明了需要进一步探索的具体研究方向。为配合本综述，我们维护着定期更新的项目页面，将深度立体匹配相关论文收录于Awesome-Deep-Stereo-Matching（https://github.com/fabiotosi92/Awesome-Deep-Stereo-Matching）知识库中。