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A Consensus-Based Framework for Distributed Bundle Adjustment

Anders P. Eriksson,J. Bastian,Tat-Jun Chin,M. Isaksson

Published 2016 in Computer Vision and Pattern Recognition

ABSTRACT

In this paper we study large-scale optimization problems in multi-view geometry, in particular the Bundle Adjustment problem. In its conventional formulation, the complexity of existing solvers scale poorly with problem size, hence this component of the Structure-from-Motion pipeline can quickly become a bottle-neck. Here we present a novel formulation for solving bundle adjustment in a truly distributed manner using consensus based optimization methods. Our algorithm is presented with a concise derivation based on proximal splitting, along with a theoretical proof of convergence and brief discussions on complexity and implementation. Experiments on a number of real image datasets convincingly demonstrates the potential of the proposed method by outperforming the conventional bundle adjustment formulation by orders of magnitude.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Computer Vision and Pattern Recognition

  • Publication date

    2016-06-01

  • Fields of study

    Mathematics, Computer Science, Engineering

  • Identifiers
    DOI 10.1109/CVPR.2016.194
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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