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.
2015
Proceedings - 2014 International Conference on 3D Vision, 3DV 2014
978-1-4799-7000-1
File in questo prodotto:
File Dimensione Formato  
07_3dv14.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 394.46 kB
Formato Adobe PDF
394.46 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1188356
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 31
  • ???jsp.display-item.citation.isi??? ND
social impact