Low Memory Cost Block-Based Belief Propagation for Stereo Correspondence

The typical belief propagation has good accuracy for stereo correspondence but suffers from large run-time memory cost. In this paper, we propose a block-based belief propagation algorithm for stereo correspondence that partitions an image into regular blocks for optimization. With independently partitioned blocks, the required memory size could be reduced significantly by 99% with slightly degraded performance with a 32times32 block size when compared to original one. Besides, such blocks are also suitable for parallel hardware implementation. Experimental results using Middlebury stereo test bed demonstrate the performance of the proposed method.