A new dynamical model for solving rotation averaging problem
Not on the map. Why?
Abstract (arXiv)
The paper analyzes the rotation averaging problem as a minimization problem for a potential function of the corresponding gradient system. This dynamical system is one generalization of the famous Kuramoto model on special orthogonal group SO(3), which is known as the non-Abelian Kuramoto model. We have proposed a novel method for finding weighted and unweighted rotation average. In order to verify the correctness of our algorithms, we have compared the simulation results with geometric and projected average using real and random data sets. In particular, we have discovered that our method gives approximately the same results as geometric average.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 35 references that are in the map. First posted on arXiv 23 Nov 2021.
Builds on
2 of 35 references.
References · 2
- 2020Shonan Rotation Averaging: Global Optimality by Surfing SO(p)ncited by 46
- 2019NeuRoRA: Neural Robust Rotation Averagingcited by 32
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Synchronization-based clustering on the unit hyperspherecited by 0
- 2026Synchronization-based clustering on the unit hypersphere5 Mar 2026