# On the Robustness of Multi-View Rotation Averaging

arXiv: https://arxiv.org/abs/2102.05454 · HTML: https://mlsanity.com/paper/2102.05454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [Robust Group Synchronization via Cycle-Edge Message Passing](https://mlsanity.com/paper/1912.11347/) (2019)
- [Lagrangian Duality in 3D SLAM: Verification Techniques and Optimal Solutions](https://mlsanity.com/paper/1506.00746/) (2015)
- [Algebraic Characterization of Essential Matrices and Their Averaging in Multiview Settings](https://mlsanity.com/paper/1904.02663/) (2019)
- [GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices](https://mlsanity.com/paper/1812.00426/) (2018)
- [Progressive Structure from Motion](https://mlsanity.com/paper/1803.07349/) (2018)
- [Averaging Essential and Fundamental Matrices in Collinear Camera Settings](https://mlsanity.com/paper/1912.00254/) (2019)
- [GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion](https://mlsanity.com/paper/1708.04814/) (2017)
- [Hybrid Camera Pose Estimation with Online Partitioning for SLAM](https://mlsanity.com/paper/1908.01797/) (2019)

## Abstract (arXiv)

Rotation averaging is a synchronization process on single or multiple rotation groups, and is a fundamental problem in many computer vision tasks such as multi-view structure from motion (SfM). Specifically, rotation averaging involves the recovery of an underlying pose-graph consistency from pairwise relative camera poses. Specifically, given pairwise motion in rotation groups, especially 3-dimensional rotation groups (eg, mathbbSO(3)), one is interested in recovering the original signal of multiple rotations with respect to a fixed frame. In this paper, we propose a robust framework to solve multiple rotation averaging problem, especially in the cases that a significant amount of noisy measurements are present. By introducing the ε-cycle consistency term into the solver, we enable the robust initialization scheme to be implemented into the IRLS solver. Instead of conducting the costly edge removal, we implicitly constrain the negative effect of erroneous measurements by weight reducing, such that IRLS failures caused by poor initialization can be effectively avoided. Experiment results demonstrate that our proposed approach outperforms state of the arts on various benchmarks.