Real-time stereo matching architecture based on 2D MRF model: a memory-efficient systolic array

There is a growing need in computer vision applications for stereopsis, requiring not only accurate distance but also fast and compact physical implementation. Global energy minimization techniques provide remarkably precise results. But they suffer from huge computational complexity. One of the main challenges is to parallelize the iterative computation, solving the memory access problem between the big external memory and the massive processors. Remarkable memory saving can be obtained with our memory reduction scheme, and our new architecture is a systolic array. If we expand it into N's multiple chips in a cascaded manner, we can cope with various ranges of image resolutions. We have realized it using the FPGA technology. Our architecture records 19 times smaller memory than the global minimization technique, which is a principal step toward real-time chip implementation of the various iterative image processing algorithms with tiny and distributed memory resources like optical flow, image restoration, etc.