An Improved Stereo Matching Network Based on Modified PSMNet

Abstract

In order to solve the stereo matching problem better, the accuracy of the algorithm in the stereo matching task needs to be improved. To tackle the problem, we proposed a Dual Spatial Pyramid Pooling Stereo Matching Network with Attention Mechanism (DSANet), a stereo matching network with better multi-scale feature extraction capability based on the basic structure of Pyramid Stereo Matching Network (PSMNet). The network consists of two main modules: the dual spatial pyramid pooling module and the attention module. The former module extracts multi-scale features from different branches at the same time. After concatenating these different features, the latter module will optimize the fused features and generate the cost volume with different branches multi-scale feature. The proposed approach was evaluated on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) 2015 dataset. Our experiment results on the KITTI 2015 dataset show that the mismatch rate of our network is 3.02%. Compared with the basic structure of PSMNet, the proposed method improves the effect of stereo matching.