Stereo matching is one of the most active research fields in computer vision. The paper introduces the categories and the performance index of stereo matching and introduces three high-speed and state-of-the-art stereo matching algorithms with different cost aggregation: fast bilateral stereo (FBS), binary stereo matching (BSM), and a non-local cost aggregation method (NLCA). By comparing the performance in terms of both quality and speed, we concluded that FSB deals with the effects of noise well; BSM is suitable for embedded devices and has a good performance with radiometric differences; NLCA combines the efficiency with the accuracy of state-of-the-art algorithms.
CITATION STYLE
Ning, K., Zhang, X., & Ming, Y. (2014). Stereo matching algorithms with different cost aggregation. Advances in Intelligent Systems and Computing, 255, 647–653. https://doi.org/10.1007/978-81-322-1759-6_74
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