Context Geometry Volume and Warping Refinement for Real-Time Stereo Matching
Ning Liu, Nannan Zhao, Yang Daniel Ou, Qingtian Wu, Xinyu Ouyang · Electronics · 2025
In the past three years, the stereo matching method based on 3D CNNs has achieved surprising results and has received more and more attention. However, most stereo matching approaches aim to improve prediction accuracy by constructing and aggregating cost volumes through extensive 3D convolutions, which not only does not fully utilize the geometric information, but also overlooks the computational speed. Thus, achieving high-accuracy, high-efficiency stereo matching has become challenging. In this paper, we present a rapid and precise stereo matching network named CGW based on 3D CNNs, which simultaneously achieves real-time functioning, considerable accuracy, and a strong generalization capability. The network is divided into two parts. The first part constructs the geometric attention cube through a lightweight feature extraction network and a lightweight 3D regularization network. The second part filters the context features using the geometric attention cube to obtain the context geometric cube, and finally, the disparity is predicted and refined to obtain the final disparity. We adopted MobileNetV3 as an efficient backbone for feature extraction and designed 3D depthwise separable convolutions with residual structures to replace traditional 3D convolutions for constructing the cost volume and performing cost aggregation, aiming to reduce the model size and improve the computational speed. Additionally, we designed the context geometric attention (CGA) module and embedded it into the lightweight 3D regularization network, as well as designed the Warped Disparity Refinement (WDR) network to further improve the disparity prediction accuracy. CGA effectively guides cost aggregation by integrating rich contextual and geometric information, while also providing feedback for feature learning to guide more efficient context feature extraction. WDR constructs a warping cost volume using the obtained initial disparity, combined with image features, the initial disparity map, and reconstruction errors, to optimize the disparity. According to the initial disparity, it searches for the accurate disparity within a refined range. By narrowing the search range, WDR simplifies the task for the network to locate the correct disparity (residual), while simultaneously improving the computational efficiency. Experiments conducted on multiple benchmark datasets showed that, compared to other fast methods, CGW has advantages in both speed and accuracy and exhibits better generalization performance.