Parallelization of Belief Propagation on Cell Processors for Stereo Vision

Abstract

Markov random field models provide a robust formulation for the stereo vision problem of inferring three-dimensional scene geometry from two images taken from different viewpoints. One of the most advanced algorithms for solving the associated energy minimization problem in the formulation is belief propagation (BP). Although BP provides very accurate results in solving stereo vision problems, the high computational cost of the algorithm hinders it from real-time applications. In recent years, multicore architectures have been widely adopted in various industrial application domains. The high computing power of multicore processors provides new opportunities to implement stereo vision algorithms. This article examines and extracts the parallelisms in the BP method for stereo vision on multicore processors. This article shows that parallelism of the algorithm can be efficiently utilized on multicore processors. The results show that parallelization on multicore processors provides a speedup for the BP algorithm of almost 15 times compared to the single-processor implementation on the PPE of the Cell BE. The experimental results also indicate that a frame rate of 6.5 frames/second is possible when implementing the parallelized BP algorithm on the multicore processor of Cell BE with one PPE and six SPEs.