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Contraction Moves for Geometric Model Fitting

  • Oliver J. Woodford
  • Minh-Tri Pham
  • Atsuto Maki
  • Riccardo Gherardi
  • Frank Perbet
  • Björn Stenger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)

Abstract

This paper presents a new class of moves, called α-expansion-contraction, which generalizes α-expansion graph cuts for multi-label energy minimization problems. The new moves are particularly useful for optimizing the assignments in model fitting frameworks whose energies include Label Cost (LC), as well as Markov Random Field (MRF) terms. These problems benefit from the contraction moves’ greater scope for removing instances from the model, reducing label costs. We demonstrate this effect on the problem of fitting sets of geometric primitives to point cloud data, including real-world point clouds containing millions of points, obtained by multi-view reconstruction.

Keywords

Point Cloud Markov Random Field Point Cloud Data Label Cost Pairwise Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Oliver J. Woodford
    • 1
  • Minh-Tri Pham
    • 1
  • Atsuto Maki
    • 1
  • Riccardo Gherardi
    • 1
  • Frank Perbet
    • 1
  • Björn Stenger
    • 1
  1. 1.Toshiba Research Europe Ltd.CambridgeUK

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