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Authors: Lorenzo Sorgi and Andrey Bushnevskiy

Affiliation: Technicolor Research & Innovation, Germany

Keyword(s): Two View Geometry, Epipolar Geometry, Perturbation Theorem, Determinant Minimization, Tetrahedron.

Abstract: Two view geometry estimation, the task of inferring the relative pose between two cameras using only the image content, is one of the fundamental and most studied problems in Computer Vision. In this paper we present a new approach for two view geometry estimation, based on the minimization of an objective function given by the overall volume of the tetrahedrons identified in 3D space by pairs of corresponding feature points. This error measure is equivalent to the determinant of a real valued square matrix, function of the point match coordinates in the camera space, and we show how to minimize it taking advantage of the Perturbation Theorem. Test performed on synthetic and real dataset confirm an increased estimation accuracy compared to the state-of-art.

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Paper citation in several formats:
Sorgi, L. and Bushnevskiy, A. (2016). Two View Geometry Estimation by Determinant Minimization. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 590-594. DOI: 10.5220/0005677405900594

@conference{visapp16,
author={Lorenzo Sorgi. and Andrey Bushnevskiy.},
title={Two View Geometry Estimation by Determinant Minimization},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP},
year={2016},
pages={590-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005677405900594},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP
TI - Two View Geometry Estimation by Determinant Minimization
SN - 978-989-758-175-5
IS - 2184-4321
AU - Sorgi, L.
AU - Bushnevskiy, A.
PY - 2016
SP - 590
EP - 594
DO - 10.5220/0005677405900594
PB - SciTePress