Title Robust Fitting for Multiple View Geometry
Authors Olof Enqvist, Erik Ask, Fredrik Kahl, Karl Åström
Full-text Available as PDF, Restricted Access
Alternative Location http://dx.doi.org/10.1007/9..., Restricted Access
Publication Lecture Notes in Computer Science (Computer Vision - ECCV 2012, Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Part I )
Year 2012
Volume 7572
Pages 738 - 751
Document type Conference paper
Conference name 12th European Conference on Computer Vision (ECCV 2012)
Conference Date 2012-10-07/2012-10-13
Conference Location Florence, Italy
Status Published
Quality controlled Yes
Language eng
Publisher Springer
Abstract English How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the num- ber of outliers can be found with an algorithm that has polynomial time- complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L2 -norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required condi- tions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.
Keywords geometry, optimization, computer vision,
ISBN/ISSN/Other ISSN: 1611-3349 (online)
ISBN: 978-3-642-33718-5 (online)

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