This paper proposes a robust method to solve the absolute rotation estimation problem, which arises in global registration of 3D point sets and in structure-from-motion. A novel cost function is formulated which inherently copes with outliers. In particular, the proposed algorithm handles both outlier and missing relative rotations, by casting the problem as a "low-rank & sparse" matrix decomposition. As a side effect, this solution can be seen as a valid and costeffective detector of inconsistent pairwise rotations. Computational efficiency and numerical accuracy, are demonstrated by simulated and real experiments
Robust Absolute Rotation Estimation via Low-rank and Sparse Matrix Decomposition / Arrigoni, Federica; Magri, Luca; Rossi, Beatrice; Fragneto, Pasqualina; Fusiello, Andrea. - (2015), pp. 491-498. (Intervento presentato al convegno 3DV 2014 tenutosi a Tokyo nel 8th-11th December 2014) [10.1109/3DV.2014.48].
Robust Absolute Rotation Estimation via Low-rank and Sparse Matrix Decomposition
Arrigoni, Federica;
2015-01-01
Abstract
This paper proposes a robust method to solve the absolute rotation estimation problem, which arises in global registration of 3D point sets and in structure-from-motion. A novel cost function is formulated which inherently copes with outliers. In particular, the proposed algorithm handles both outlier and missing relative rotations, by casting the problem as a "low-rank & sparse" matrix decomposition. As a side effect, this solution can be seen as a valid and costeffective detector of inconsistent pairwise rotations. Computational efficiency and numerical accuracy, are demonstrated by simulated and real experimentsFile | Dimensione | Formato | |
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