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TRPLP – Trifocal Relative Pose From Lines at Points

Authors :
Benjamin B. Kimia
Ricardo Fabbri
Anton Leykin
Elias P. Tsigaridas
Charles W. Wampler
David da Costa de Pinho
Tomas Pajdla
Jonathan D. Hauenstein
Peter Giblin
Margaret H. Regan
Timothy Duff
Hongyi Fan
State University of Rio de Janeiro
Georgia Institute of Technology [Atlanta]
School of Mathematics - Georgia Institute of Technology
Brown University
University of Notre Dame [Indiana] (UND)
Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF)
OUtils de Résolution Algébriques pour la Géométrie et ses ApplicatioNs (OURAGAN)
Inria de Paris
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
University of Liverpool
Department of Computer Science (Brown University)
Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC)
Czech Technical University in Prague (CTU)
Source :
CVPR 2020-IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2020, Seattle / Virtual, United States. pp.12070-12080, ⟨10.1109/CVPR42600.2020.01209⟩, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), CVPR
Publication Year :
2020
Publisher :
HAL CCSD, 2020.

Abstract

Code available at http://github.com/rfabbri/minus; International audience; We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Grobner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We show in simulated experiments that our solvers are numerically robust and stable under image noise. We show in real experiment that (i) SIFT features provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches where the state of the art structure from motion initialization fails.

Details

Language :
English
ISBN :
978-1-72817-168-5
ISBNs :
9781728171685
Database :
OpenAIRE
Journal :
CVPR 2020-IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2020, Seattle / Virtual, United States. pp.12070-12080, ⟨10.1109/CVPR42600.2020.01209⟩, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), CVPR
Accession number :
edsair.doi.dedup.....f1b4e16d363ff3db852e9ca9f50a34d7
Full Text :
https://doi.org/10.1109/CVPR42600.2020.01209⟩