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Using convolutional neural network for matching cost computation in stereo matching
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Paper Abstract

Stereo matching is one of the most important computer vision tasks. Several methods can be used to compute a matching cost of two pictures. This paper proposes a method that uses convolutional neural networks to compute the matching cost. The network architecture is described as well as teaching process. The matching cost metric based on the result of neural network is applied to base method which uses support points grid (ELAS). The proposed method was tested on Middlebury benchmark images and showed an accuracy improvement compared to the base method.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420P (18 December 2019); doi: 10.1117/12.2548061
Show Author Affiliations
Aleksei Denisov, ITMO Univ. (Russian Federation)
Yan Wang, ITMO Univ. (Russian Federation)
Andrey Zhdanov, ITMO Univ. (Russian Federation)
Sergei Bykovskii, ITMO Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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