In this paper we propose a novel solution to the multi-view matching problem that, given a set of noisy pairwise correspondences, jointly updates them so as to maximize their consistency. Our method is based on a spectral decomposition, resulting in a closed-form efficient algorithm, in contrast to other iterative techniques that can be found in the literature. Experiments on both synthetic and real datasets show that our method achieves comparable or superior accuracy to state-of-the-art algorithms in significantly less time. We also demonstrate that our solution can efficiently handle datasets of hundreds of images, which is unprecedented in the literature.

Practical and Efficient Multi-view Matching / Maset, Eleonora; Arrigoni, Federica; Fusiello, Andrea. - (2017), pp. 4578-4586. (Intervento presentato al convegno ICCV 2017 tenutosi a Venezia nel 22nd-29th October 2017) [10.1109/ICCV.2017.489].

Practical and Efficient Multi-view Matching

Arrigoni, Federica;
2017-01-01

Abstract

In this paper we propose a novel solution to the multi-view matching problem that, given a set of noisy pairwise correspondences, jointly updates them so as to maximize their consistency. Our method is based on a spectral decomposition, resulting in a closed-form efficient algorithm, in contrast to other iterative techniques that can be found in the literature. Experiments on both synthetic and real datasets show that our method achieves comparable or superior accuracy to state-of-the-art algorithms in significantly less time. We also demonstrate that our solution can efficiently handle datasets of hundreds of images, which is unprecedented in the literature.
2017
2017 IEEE International Conference on Computer Vision: Proceedings
Piscataway, NJ
IEEE
978-1-5386-1032-9
Maset, Eleonora; Arrigoni, Federica; Fusiello, Andrea
Practical and Efficient Multi-view Matching / Maset, Eleonora; Arrigoni, Federica; Fusiello, Andrea. - (2017), pp. 4578-4586. (Intervento presentato al convegno ICCV 2017 tenutosi a Venezia nel 22nd-29th October 2017) [10.1109/ICCV.2017.489].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/272074
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