Multi-Scale Cascade Disparity Refinement Stereo Network

Abstract

Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, this paper presents MCDRNet, which combines traditional methods with neural networks to achieve real-time and accurate stereo matching results. Concretely, our network first generates a rough disparity map based on the traditional ADCensus algorithm. Then we design a novel Multi-Scale Cascade Network to refine the disparity map from coarse to fine. We evaluate our best-trained model on the KITTI official website. The results show that our network is much faster than most current top-performing methods(31×than CSPN, 56×than GANet, etc.). Meanwhile, it is more accurate than traditional stereo methods(SGM, SPS-St) and other fast 2D convolution networks(Fast DS-CS, DispNetC, etc.), demonstrating the rationalities and feasibilities of our method.