Compact Fundamental Matrix Computation

Authors:
Kenichi Kanatani;Yasuyuki Sugaya
Affiliations:
Department of Computer Science, Okayama University, Okayama, Japan 700-8530;Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Japan 441-8580
Venue:
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Year:
2009

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Abstract

A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the strict maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.