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 F.;Magri L.;Fragneto P.;
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 experiments.File in questo prodotto:
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