REAL-TIME DENSE DISPARITY ESTIMATION USING CUDA'S API

In this paper, we present a real-time dense disparity map estimation based on beliefs propagation inference algorithm. While being real-time, our implementation generates high quality disparity maps. Despite the high complexity of the calculations beliefs propagation involves, our implementation on graphics processor using CUDA API makes more than 100 times speedup compared to CPU implementation. We tested our experimental results in the Middlebury benchmark and obtained good results among the real-time algorithms. We use several programming techniques to reduce the number of iterations to convergence and memory usage in order to maintain real-time performance.