Fast RANSAC hypothesis generation for essential matrix estimation
dc.contributor.author | Botterill, T. | |
dc.contributor.author | Mills, S. | |
dc.contributor.author | Green, R. | |
dc.date.accessioned | 2012-02-06T22:33:17Z | |
dc.date.available | 2012-02-06T22:33:17Z | |
dc.date.issued | 2011 | en |
dc.description.abstract | The RANSAC framework is often used to estimate the relative pose of two cameras from outlier-contaminated point correspondences, via the essential matrix, however this is computationally expensive due the cost of computing essential matrices from many sets of five to seven correspondences. The leading contemporary 5-point solver (Nister, 2004) is slow because of the expensive linear algebra decompositions and polynomial solve which are required. To avoid these costs we propose to use Levenberg-Marquardt optimisation on a manifold to find a subset of the compatible essential matrices. The proposed algorithm finds essential matrices at a higher rate than closed-form approaches, and reduces the time needed to find relative poses using RANSAC by 25%. The second contribution of this paper is to apply the optimisations used in 5-point solvers to the classic 7-point algorithm. RANSAC using the optimised 7-point algorithm is considerably faster than 5-point RANSAC (unless planar point configurations are common), despite the increased number of iterations necessary. | en |
dc.identifier.citation | Botterill, T., Mills, S., Green, R. (2011) Fast RANSAC hypothesis generation for essential matrix estimation. Noosa, Queensland, Australia: 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 5-9 Dec 2011. | en |
dc.identifier.uri | http://hdl.handle.net/10092/6269 | |
dc.language.iso | en | |
dc.publisher | University of Canterbury. Computer Science and Software Engineering | en |
dc.rights.uri | https://hdl.handle.net/10092/17651 | en |
dc.subject.anzsrc | Fields of Research::49 - Mathematical sciences::4904 - Pure mathematics::490401 - Algebra and number theory | en |
dc.subject.anzsrc | Field of Research::08 - Information and Computing Sciences::0802 - Computation Theory and Mathematics::080201 - Analysis of Algorithms and Complexity | en |
dc.subject.anzsrc | Field of Research::08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080104 - Computer Vision | en |
dc.title | Fast RANSAC hypothesis generation for essential matrix estimation | en |
dc.type | Conference Contributions - Published |
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