Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
This work addresses stereo matching for computer vision applications, offering a novel approach but is incremental in advancing geometric integration.
The paper tackled the problem of stereo matching by integrating both intra-view and cross-view geometric knowledge, which prior methods had overlooked, resulting in improved disparity estimation as demonstrated in experiments.
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.