Parallel and Distributed Systems, International Conference on (2012)
Singapore, Singapore Singapore
Dec. 17, 2012 to Dec. 19, 2012
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 stereo-matching 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.
GPUs, Stereo Matching, Cost Aggregation, Performance Optimization, Performance Modeling, OpenCL
J. Fang, A. L. Varbanescu, J. Shen, H. Sips, G. Saygili and L. Van Der Maaten, "Accelerating Cost Aggregation for Real-Time Stereo Matching," 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), Singapore, 2012, pp. 472-481.