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GPU optimization of the SGM stereo algorithm

GPU hardware architectures have evolved into a suitable platform for the hardware acceleration of complex computing tasks. Stereo vision is one such task where acceleration is desirable for robotic and automotive systems. Much research was invested in developing stereo vision algorithms with increased quality, but real-time implementations are still lacking. In this work we focus on creating a real-time dense stereo reconstruction system. We selected the Semi-global Matching method as the basis of our system due to its high quality and reduced computational complexity. The Census transform is selected as the matching metric because our results show that it can reduce the matching errors for traffic images compared to classical solutions. We also present two modifications to the original Semi-Global algorithm to improve the sub-pixel accuracy and the execution time. The system was implemented and evaluated on a current generation GPU with a running time of 19ms for image having the resolution 512×383.