Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
arXiv:1505.06079 · doi:10.1016/j.cviu.2018.08.001
Abstract
This paper deals with the rotation synchronization problem, which arises in global registration of 3D point-sets and in structure from motion. The problem is formulated in an unprecedented way as a "low-rank and sparse" matrix decomposition that handles both outliers and missing data. A minimization strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against state-of-the-art algorithms on simulated and real data. The results show that R-GoDec is the fastest among the robust algorithms.
The material contained in this paper is part of a manuscript submitted to CVIU
References in corpus (5)
Cited by in corpus (10)
- Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
- Registration of multi-view point sets under the perspective of expectation-maximization
- RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
- Message Passing Least Squares Framework and its Application to Rotation Synchronization
- Learning to Communicate and Correct Pose Errors
- Learning Transformation Synchronization
- 3DMNDT:3D multi-view registration method based on the normal distributions transform
- Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration
- Effective multi-view registration of point sets based on student's t mixture model
- Path-Invariant Map Networks