Real-Time Semi-Global Matching Disparity Estimation on the GPU

This paper presents the design, implementation and evaluation of new parallelization schemes for performing dense disparity estimation based on non-parametric rank transform and semi-global matching on Graphics Processing Units (GPUs). A detailed analysis of the performance limitating factors (memory throughput, instruction throughput, etc.) for each part of the parallel implementation is performed. Thus, a highly optimized mapping for each parallelization scheme onto the resources of the GPU is obtained. The resulting implementation performs disparity estimation at 27 frames per second for 1024x768 pixel images with 128 disparity levels on a Nvidia Tesla C2050 GPU.