  Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging
algorithm that is guaranteed to recover globally optimal solutions under mild
assumptions on the measurement noise. Our method employs semidefinite
relaxation in order to recover provably globally optimal solutions of the
rotation averaging problem. In contrast to prior work, we show how to solve
large-scale instances of these relaxations using manifold minimization on (only
slightly) higher-dimensional rotation manifolds, re-using existing
high-performance (but local) structure-from-motion pipelines. Our method thus
preserves the speed and scalability of current SFM methods, while recovering
globally optimal solutions.
