Adaptive slice stereo matching network

The convolutional neural network (CNN) method is widely used in the field of stereo matching, and common CNNs regress disparity information by forming cost volumes. However, during the construction of cost volume, various types of information are integrated and combined; these pieces of information are input into an aggregation network to enable full learning when there is no significant feature guidance, which is a hypodynamic approach. To address this problem, we propose a stereo-matching slice method. Our core idea is to improve the salience of cost features through slicing, enhance feature self-guidance and self-interaction, and alleviate the mismatch between network aggregation capability and cost information. First, we use a group-wise correlation stereo network as the baseline; integrate the slice idea into feature extraction, cost volume construction, and feature aggregation; and propose the Gwc-Slice. Specifically, the residual extraction network is divided into seven groups of feature extractors by channel, the outputs of the feature extractors are used to construct slice cost volumes using the adaptive slice method, and different slice cost volumes are aggregated through different scales to enhance feature interactions and guidance between slice cost volumes. Finally, a slice network is designed to further optimize Gwc-Slice and verify the effectiveness of the slice method. The test results based on the SceneFlow, KITTI2015, KITTI2012, and Middlebury datasets show that our approach can significantly improve the prediction accuracy of the baseline and achieve competitive results.