Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

  • Minh-Tri Pham
  • Oliver J. Woodford
  • Frank Perbet
  • Atsuto Maki
  • Riccardo Gherardi
  • Björn Stenger
  • Roberto Cipolla
Part of the Studies in Computational Intelligence book series (SCI, volume 411)

Abstract

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this space—the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.

Keywords

Point Cloud Visual Word Registration Rate Inference Method Training Feature 
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 Berlin Heidelberg 2013

Authors and Affiliations

  • Minh-Tri Pham
    • 1
  • Oliver J. Woodford
    • 1
  • Frank Perbet
    • 1
  • Atsuto Maki
    • 1
  • Riccardo Gherardi
    • 1
  • Björn Stenger
    • 1
  • Roberto Cipolla
    • 2
  1. 1.Toshiba Research Europe LtdCambridgeUK
  2. 2.Department of EngineeringUniversity of CambridgeCambridgeUK

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