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This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scanline optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system. 1

Year: 2013
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