MPA‐Net: multi‐path attention stereo matching network

Abstract

A novel learning-based end-to-end network for stereo matching, named Multi-path Attention Stereo Matching (MPA-Net), is introduced in this study. Different from existing methods, the multi-path attention aggregation module is designed firstly, named MPA, which is a unified structure using three different parallel layers with a respective attention mechanism to extract the multi-scale informational features. Secondly, the method of cost volume construction, which differs from the traditional stereo matching methods, is extended. And then, the absolute difference between two input features is calculated. Furthermore, a u-shaped structure with 3D attention gate is selected as the encoder-decoder module. Specifically, the module is used to fuse the encoding features to their corresponding decoding features under the supervision of the authors' attention gate with skip-connection, and thus exploit more significant information for matching cost regularisation and disparity prediction. Finally, specific experiments are conducted to evaluate their network on SceneFlow, KITTI2012 and KITTI2015 data sets. The results show that their method achieves a better improvement in disparity maps prediction compared with some existing state-of-the-art methods on KITTI benchmark.