Belief propagation implementation using CUDA on an NVIDIA GTX 280

Abstract

Disparity map generation is a significant component of vision-based driver assistance systems. This paper describes an efficient implementation of a belief propagation algorithm on a graphics card (GPU) using CUDA (Compute Uniform Device Architecture) that can be used to speed up stereo image processing by between 30 and 250 times. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury stereo website, and real-world stereo sequences, self-recorded with the research vehicle of the enpeda project at The University of Auckland. This paper provides implementation details, primarily concerned with the inequality constraints, involving the threads and shared memory, required for efficient programming on a GPU. © Springer-Verlag Berlin Heidelberg 2009.