Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap, we propose a novel Intra-view and Cross-view Geometric knowledge learning Network (ICGNet), specifically crafted to assimilate both intra-view and cross-view geometric knowledge. ICGNet harnesses the power of interest points to serve as a channel for intra-view geometric understanding. Simultaneously, it employs the correspondences among these points to capture cross-view geometric relationships. This dual incorporation empowers the proposed ICGNet to leverage both intra-view and cross-view geometric knowledge in its learning process, substantially improving its ability to estimate disparities. Our extensive experiments demonstrate the superiority of the ICGNet over contemporary leading models.

翻译：几何知识已被证实对立体匹配任务具有显著益处。然而，此前将几何先验融入立体匹配算法的尝试，主要聚焦于单幅图像的几何知识，而忽略了诸如遮挡与匹配唯一性等关键跨视图因素。为弥补这一不足，本文提出了一种新颖的视图内与跨视图几何知识学习网络（ICGNet），专门设计用于同时吸收视图内与跨视图几何知识。ICGNet利用兴趣点作为视图内几何理解的媒介，同时借助这些点间的对应关系捕捉跨视图几何关联。这种双重融入机制使得ICGNet能够在学习过程中综合利用视图内与跨视图几何知识，从而显著提升其视差估计能力。大量实验表明，ICGNet的性能优于当前主流模型。