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Shonan Rotation Averaging: Global Optimality by Surfing \(SO(p)^n\)

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12351)

Abstract

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.

Supplementary material

504443_1_En_18_MOESM1_ESM.pdf (642 kb)
Supplementary material 1 (pdf 642 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Australian National UniversityCanberraAustralia

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