Multi-dimensional Attention Feature Aggregation Stereo Matching Algorithm

Existing deep learning-based stereo matching algorithms lack effective information interaction in the learning and reasoning process, and there is difference in feature dimension between feature extraction and cost aggregation, resulting in less and single application of attention methods in stereo matching networks. In order to solve these problems, a multi-dimensional attention feature aggregation stereo matching algorithm was proposed. The two-dimensional (2D) attention residual module is designed by introducing the adaptive 2D attention residual unit without dimensionality reduction into the original residual network. Local cross-channel interaction and extraction of salient information provide abundant and effective features for matching cost calculation. The three-dimensional (3D) attention hourglass aggregation module is constructed by designing a 3D attention hourglass unit with a stacked hourglass structure as the backbone. It captures multi-scale geometric context information and expand the multi-dimensional attention mechanism, adaptively aggregating and recalibrating cost volumes from different network depths. The proposed algorithm is evaluated on three standard datasets and compared with related algorithms. The experimental results show that the proposed algorithm has higher accuracy in predicting disparity and has better effect on unobstructed salient objects. © 2022 Science Press. All rights reserved.