SORT-SGM: Subpixel Optimized Real-Time Semiglobal Matching for Intelligent Vehicles

Abstract

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, an original stereo reconstruction system that is designed for automotive applications is presented. The system is based on the semiglobal matching algorithm (SGM), which is widely known for its high quality and potential for real-time implementation. Several improvements that target the matching, disparity optimization, and disparity refinement steps are proposed. Pixel-level matching uses the census transform because of its invariance to intensity differences due to camera bias or gain that affects the images. The huge memory bandwidth requirements for the SGM disparity optimization step are reduced through a new integration strategy. At the subpixel level, accuracy is increased by devising a new methodology for generating dedicated subpixel interpolation functions. Using this methodology, two novel subpixel interpolation functions for the SGM algorithm are implemented and evaluated. The proposed algorithm has been implemented on a graphics processing unit in the Compute Unified Device Architecture (CUDA). The result is an increased speed and accuracy algorithm profiled for complex real-traffic scenarios. The proposed algorithm has been evaluated at a large scale, and evidence that was collected from both standard benchmarks and real-world images confirm the findings and show a significant improvement over existing solutions.