title: Camera Pose and Focal Length Estimation Using Regularized Distance Constraints
metadata.name: dc.contributor.author
metadata.value: Kanaeva, Ekaterina
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Gurevich, Lev
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Vakhitov, Alexander
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2016-04-11T17:24:48Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2016-04-11T17:24:48Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2015-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Ekaterina Kanaeva, Lev Gurevich and Alexander Vakhitov. Camera Pose and Focal Length Estimation Using Regularized Distance Constraints. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 162.1-162.12. BMVA Press, September 2015.
metadata.lang: -
metadata.name: dc.identifier.isbn
metadata.value: 1-901725-53-7
metadata.lang: -
metadata.name: dc.identifier.other
metadata.value: DOI 10.5244/C.29.162
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/2058
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: We propose a new method for camera pose estimation with unknown focal length (PnPf problem). We combine projection equations and distance constraints in a single statistically significant cost function in the form of least squares. We fix the space of the search as a linear combination of several right singular vectors of the least squares system matrix. We use linear programming techniques to find feasible solutions faster. Then we do nonlinear refinement with Levenberg-Marquardt. Numerical experiments demonstrated that the method is faster than the state-of-the-art methods for point numbers up to several hundreds, and real-life structure-from-motion demonstrates its applicability for models having $10^3$-$10^5$ points. It has the same accuracy of estimates as the the state-of-the-art methods. We show that the method offers a tradeoff between speed and accuracy, allowing the estimation to run several times faster while slightly increasing the mean reprojection error.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Camera Pose and Focal Length Estimation Using Regularized Distance Constraints
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: bmvc_final_alg.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/2058/1/bmvc_final_alg.pdf
resources.format: Adobe PDF
resources.size: 8,57 MB