High Accuracy Fundamental Matrix Computation and Its Performance Evaluation

Authors:
Kenichi Kanatani;Yasuyuki Sugaya
Affiliations:
The author is with the Department of Computer Science, Okayama University, Okayama-shi, 700--8530 Japan. E-mail: kanatani@suri.it.okayama-u.ac.jp,;The author is with the Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi-shi, 441--8580 Japan.
Venue:
IEICE - Transactions on Information and Systems
Year:
2007

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Abstract

We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization. We also introduce Gauss-Newton iterations as a new method for fundamental matrix computation. For initial values, we test random choice, least squares, and Taubin's method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence properties.