A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching |
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Authors: | Minglun Gong Ruigang Yang Liang Wang Mingwei Gong |
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Affiliation: | (1) Department of Math & Computer Science, Laurentian University, Sudbury, ON, Canada;(2) Department of Computing Science, University of Kentucky, Lexington, KY, USA;(3) Department of Computer Science, University of Calgary, Calgary, AB, Canada |
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Abstract: | Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most
real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local
approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that,
through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local
approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate
whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform
under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing
power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics
hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing
speed and result quality are reported. |
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Keywords: | real-time stereo matching cost aggregation algorithms programmable graphics hardware |
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