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.

提出了一种新颖的 “Intra-view and Cross-view Geometric knowledge learning Network (ICGNet)” 模型，以整合单景图片的几何知识和跨景因素（包括遮挡和匹配独特性）的几何知识，并通过利用兴趣点和它们之间的对应关系来增强几何知识的学习过程，从而在估计视差的能力上显著提高，实验证明了 ICGNet 模型相对于其他流行模型的优越性。