Real-time Semi-Global Matching using Segmentation and Plane Fitting for Improved Accuracy on the GPU

The suitability of stereo algorithms for intelligent vehicle applications is conditioned by their ability to compute dense, accurate disparity maps in real time. In this paper a real-time stereo reconstruction system designed for automotive applications is presented. The system is based on the Semi-Global Matching algorithm, widely known for its high quality and potential for real-time implementation. The accuracy of this algorithm is increased by a new segmentation and plane fitting based approach which is used for refining the estimated disparities. The main contribution is scalable solution for obtaining a significant enhancement of the matching quality in textureless or otherwise difficult areas that also maintains the algorithm's real-time processing capabilities. In this sense, a pixel-centered approach is proposed and compared with two existing plane fitting strategies. The different approaches are also analyzed from the perspective of matching quality and parallelization cost on GPU architectures. Results on the Middlebury benchmark and real-world images confirm the strength of the new method. The complete stereo reconstruction system was implemented and evaluated on a current generation GPU.