Quantum Multiple Rotation Averaging

Shuteng Wang, Natacha Kuete Meli, Michael Moeller, Vladislav Golyanik

Keywords: quantum annealing, rotation averaging

TL;DR: a new rotation averaging algorithm using quantum annealing

Abstract: Multiple rotation averaging (MRA) is a fundamental optimization problem in 3D vision and robotics that aims to recover globally consistent absolute rotations from noisy relative measurements. Established classical methods, such as L1-IRLS and Shonan, face limitations including local minima susceptibility and reliance on convex relaxations that fail to preserve the exact manifold geometry, leading to reduced accuracy in high-noise scenarios. We introduce IQARS~(**I**terative **Q**uantum **A**nnealing for **R**otation **S**ynchronization), the first algorithm that reformulates MRA as a sequence of local quadratic non-convex sub-problems executable on quantum annealers after binarization, to leverage inherent hardware advantages. IQARS removes convex relaxation dependence and better preserves non-Euclidean rotation manifold geometry while leveraging quantum tunneling and parallelism for efficient solution space exploration. We evaluate IQARS's performance on synthetic and real-world datasets. While current annealers remain in their nascent phase and only support solving problems of limited scale with constrained performance, we observed that IQARS on D-Wave annealers can already achieve $\approx$12\% higher accuracy than Shonan---the best-performing classical method evaluated empirically. Project page: https://4dqv.mpi-inf.mpg.de/QMRA/.

Submission Number: 7