Accelerating Cost Aggregation for Real-Time Stereo Matching

Jianbin Fang, Ana Lucia Varbanescu, Jie Shen, Henk Sips, Gorkem Saygili, Laurens van der Maaten
Parallel and Distributed Systems Group, Delft University of Technology, Delft, the Netherlands
IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS’12), 2012
@inproceedings{fang2012accelerating,

   author={Jianbin Fang, Ana Lucia Varbanescu, Jie Shen, Henk Sips, Gorkem Saygili and Laurens van der Maaten},

   title={Accelerating Cost Aggregation for Real-Time Stereo Matching},

   booktitle={Proceedings of IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS’12)},

   year={2012},

   location={Singapore},

   url={http://www.pds.ewi.tudelft.nl/fileadmin/pds/homepages/fang/papers/icpads2012.pdf},

   topic={Parallel Programming},

   group={PDS}

}

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Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. The most time-consuming part of stereomatching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for three commonly used cost aggregators on many-core processors. We perform typical optimizations on the kernels, which leads to significant performance improvement (up to two orders of magnitude). Finally, we present a performance model for the three aggregators to predict the aggregation speed for a given pair of input images on a given architecture. Experimental results validate our model with an acceptable error margin (an average of 10.4%). We conclude that GPU-like many-cores are excellent platforms for accelerating stereo matching.
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