  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.
