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Comparison of FPGA and GPU implementations of real-time stereo vision

R. Kalarot, J. Morris
Department of Computer Science, The University of Auckland, Auckland, New Zealand
In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (June 2010), pp. 9-15.

@conference{kalarot2010comparison,

   title={Comparison of FPGA and GPU implementations of real-time stereo vision},

   author={Kalarot, R. and Morris, J.},

   booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on},

   pages={9–15},

   year={2010},

   organization={IEEE}

}

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Real-time stereo vision systems have many applications – from autonomous navigation for vehicles through surveillance to materials handling. Accurate scene interpretation depends on an ability to process high resolution images in real-time, but, although the calculations for stereo matching are basically simple, a practical system needs to evaluate at least 109 disparities every second – beyond the capability of a single processor. Stereo correspondence algorithms have high degrees of inherent parallelism and are thus good candidates for parallel implementations. In this paper, we compare the performance obtainable with an FPGA and a GPU to understand the trade-off between the flexibility but relatively low speed of an FPGA and the high speed and fixed architecture of the GPU. Our comparison highlights the relative strengths and limitations of the two systems. Our experiments show that, for a range of image sizes, the GPU manages 2×10^9 disparities per second, compared with 2.6×10^9 disparities per second for an FPGA.
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