The Implementation of Parallel Speed up Stereo Matching Based on Reduced Graph Cuts

Abstract

The Many stereo matching problems can be converted to energy minimization problem, by establishment of special network graph to obtain the minimum graph cut, and then obtaining the optimal solution. For graph cuts algorithm, complete network graph include all vertices and disparity edges, the computation of time and space is huge. In this paper, we put forward a method by combing local and global stereo matching, set up a reduced network graph by the possible disparities values for each pixel, and then global optimization, to slove the maximum flow polynomial through CUDA parallel computing, greatly reduced the consumption of time and space.