Improvement of AnyNet-based end-to-end phased binocular stereo matching network

To improve the accuracy of the parallax maps obtained by the binocular stereo matching network for better 3D reconstruction, this thesis is based on the AnyNet network, with the corresponding optimization to get MyNet. The Hourglass structure allows for the acquisition of multiscale image information. The original 3D Conv is replaced by a 3D residual block in the parallax network to avoid the gradient disappearance or explosion and model degradation problems of traditional convolutional neural network models, thus further improving the cost volume obtained. The experiments were tested on the KITTI2012 and KITTI2015 datasets and showed some improvement compared to the original algorithm. The experimental results show that the accuracy of the parallax map has improved significantly with almost the same time spent.