Real-time Adaptation for Stereo Matching

Stereo matching is a classical computer vision problem. The accuracy and real-time performance of the traditional method or convolutional neural network (CNN) method for stereo matching cannot meet the requirements of online application. Therefore, a real-time adaptive stereo matching network algorithm was proposed in this paper. The accuracy and real-time performance was improved by introducing a new lightweight and effective architecture Modularly Adaptive Stereo Network (MASNet), embedded an unsupervised loss function and residual refinement module. The experimental results show that the proposed method is more accurate than the model with similar complexity, and the processing speed of about 25 frames per second on average meets the requirements of online usage.