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Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$
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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.
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Cited by 1 Pith paper
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Making Rotation Averaging Fast and Robust with Anisotropic Coordinate Descent
ACD is a fast block coordinate descent method for anisotropic rotation averaging, achieving state-of-the-art accuracy on SfM datasets with a simple projection update.
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