Real-time stereo vision: Optimizing Semi-Global Matching

Semi-Global Matching (SGM) is arguably one of the most popular algorithms for real-time stereo vision. It is already employed in mass production vehicles today. Thinking of applications in intelligent vehicles (and fully autonomous vehicles in the long term), we aim at further improving SGM regarding its accuracy. In this study, we propose a straight-forward extension of the algorithm´s parametrization. We consider individual penalties for different path orientations, weighted integration of paths, and penalties depending on intensity gradients. In order to tune all parameters, we applied evolutionary optimization. For a more efficient offline optimization and evaluation, we implemented SGM on graphics hardware. We describe the implementation using CUDA in detail. For our experiments, we consider two publicly available datasets: the popular Middlebury benchmark as well as a synthetic sequence from the .enpeda. project. The proposed extensions significantly improve the performance of SGM. The number of incorrect disparities was reduced by up to 27.5 % compared to the original approach, while the runtime was not increased.