This paper casts the global registration of multiple 3D point-sets into a low-rank and sparse decomposition problem. This neat mathematical formulation caters for missing data, outliers and noise, and it benefits from a wealth of available decomposition algorithms that can be plugged-in. Experimental results show that this approach compares favourably to the state of the art in terms of precision and speed, and it outperforms all the analysed techniques as for robustness to outliers

Global registration of 3D point sets via LRS decomposition / Arrigoni, Federica; Rossi, Beatrice; Fusiello, Andrea. - 9908:(2016), pp. 489-504. (Intervento presentato al convegno ECCV 2016 tenutosi a Amsterdam nel 8th-16th October 2016) [10.1007/978-3-319-46493-0_30].

Global registration of 3D point sets via LRS decomposition

Arrigoni, Federica;
2016-01-01

Abstract

This paper casts the global registration of multiple 3D point-sets into a low-rank and sparse decomposition problem. This neat mathematical formulation caters for missing data, outliers and noise, and it benefits from a wealth of available decomposition algorithms that can be plugged-in. Experimental results show that this approach compares favourably to the state of the art in terms of precision and speed, and it outperforms all the analysed techniques as for robustness to outliers
2016
Computer Vision: ECCV 2016 14th European Conference Proceedings 4
Cham
Springer Verlag
9783319464923
978-3-319-46493-0
Arrigoni, Federica; Rossi, Beatrice; Fusiello, Andrea
Global registration of 3D point sets via LRS decomposition / Arrigoni, Federica; Rossi, Beatrice; Fusiello, Andrea. - 9908:(2016), pp. 489-504. (Intervento presentato al convegno ECCV 2016 tenutosi a Amsterdam nel 8th-16th October 2016) [10.1007/978-3-319-46493-0_30].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/272058
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