English

ReSync: Riemannian Subgradient-based Robust Rotation Synchronization

Optimization and Control 2023-12-07 v2 Machine Learning

Abstract

This work presents ReSync, a Riemannian subgradient-based algorithm for solving the robust rotation synchronization problem, which arises in various engineering applications. ReSync solves a least-unsquared minimization formulation over the rotation group, which is nonsmooth and nonconvex, and aims at recovering the underlying rotations directly. We provide strong theoretical guarantees for ReSync under the random corruption setting. Specifically, we first show that the initialization procedure of ReSync yields a proper initial point that lies in a local region around the ground-truth rotations. We next establish the weak sharpness property of the aforementioned formulation and then utilize this property to derive the local linear convergence of ReSync to the ground-truth rotations. By combining these guarantees, we conclude that ReSync converges linearly to the ground-truth rotations under appropriate conditions. Experiment results demonstrate the effectiveness of ReSync.

Keywords

Cite

@article{arxiv.2305.15136,
  title  = {ReSync: Riemannian Subgradient-based Robust Rotation Synchronization},
  author = {Huikang Liu and Xiao Li and Anthony Man-Cho So},
  journal= {arXiv preprint arXiv:2305.15136},
  year   = {2023}
}

Comments

Accepted for publication in NeurIPS 2023

R2 v1 2026-06-28T10:44:34.839Z