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Real Time Stereo Vision Using Exponential Step Cost Aggregation On GPU

Wei Yu, Tsuhan Chen, James C. Hoe
Carnegie Mellon University
16th IEEE International Conference on Image Processing (ICIP), 2009, p.4281-4284

@conference{yu2010real,

   title={Real time stereo vision using exponential step cost aggregation on GPU},

   author={Yu, W. and Chen, T. and Hoe, J.C.},

   booktitle={Image Processing (ICIP), 2009 16th IEEE International Conference on},

   pages={4281–4284},

   issn={1522-4880},

   year={2010},

   organization={IEEE}

}

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In this paper, we propose a local cost aggregation approach for real time stereo vision on a graphics processing unit (GPU). Recent research shows that local approaches based on carefully designed cost aggregation strategies can outperform many global approaches. Among those local aggregation approaches, adaptive-weight window produces the best quality disparity map under real-time constraint, but it is slower than other local approaches. We propose a very fast adaptive-weight aggregation method based on exponential step information propagation. The basic idea is to propagate information from long distance pixels within a few iterations. We also discuss important techniques of efficient implementation on GPU platform, which result in 10.5x speed up than a straightforward implementation. Compared to existing real time adaptive-weight approach, our technique reduces the computation time by more than half at improved accuracy. Detailed experimental results show that our technique is Pareto-optimal among existing real time or near real time stereo algorithms in the accuracy-speed trade-off space.
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