Real-Time High-Quality Stereo Matching System on a GPU
This work addresses the need for efficient and accurate stereo matching in real-time applications, such as robotics or autonomous vehicles, though it is incremental as it builds on existing GPU methods.
The paper tackles the trade-off between error rate and processing speed in GPU-based stereo vision systems by proposing a system that achieves real-time processing at 40 fps for high-resolution images while maintaining the lowest error rate among GPU systems faster than 30 fps.
In this paper, we propose a low error rate and real-time stereo vision system on GPU. Many stereo vision systems on GPU 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 1436x992 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.