Learning Intra-View and Cross-View Geometric Knowledge for Stereo Matching

Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to in-tegrate 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 un...

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Bibliographic Details
Main Authors: Gong, Rui, Liu, Weide, Gu, Zaiwang, Yang, Xulei, Cheng, Jun
Format: Conference Proceeding
Language:English
Subjects:
Codes
Computational modeling
Computer vision
Knowledge engineering
Pattern matching
Stereo Matching
Online Access:Request full text
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Summary:Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to in-tegrate 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 geo-metric knowledge. ICGNet harnesses the power of interest points to serve as a channel for intra-view geometric understanding. Simultaneously, it employs the correspon-dences 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. The code will be available at https://github.com/DFSDDDDDl199/ICGNet.
ISSN:2575-7075
DOI:10.1109/CVPR52733.2024.01961