Real-Time High-Quality Stereo Matching System on a GPU
Qiong Chang, Tsutomu Maruyama · 2018
In this paper, we propose a low error rate and realtime stereo vision system on G PU. Many stereo vision systems on G PU have been proposed to date. In those systems, the error rates and the processing speed are in trade-off relationship. We propose a real-time stereo vision system on GPU for the high resolution images. This system also maintains a low error rate compared to other fast systems. In our approach, we have implemented the cost aggregation (CA), cross-checking and median filter on GPU in order to realize the real-time processing. Its processing speed is 40 fps for 1436×992 pixels images when the maximum disparity is 145, and its error rate is the lowest among the GPU systems which are faster than 30 fps.