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

  • Chapter
Machine Learning for Computer Vision

Part of the book series: Studies in Computational Intelligence ((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.

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Author information

Authors and Affiliations

  1. Toshiba Research Europe Ltd, Cambridge, UK

    Minh-Tri Pham, Oliver J. Woodford, Frank Perbet, Atsuto Maki, Riccardo Gherardi & Björn Stenger

  2. Department of Engineering, University of Cambridge, Cambridge, UK

    Roberto Cipolla

Authors
  1. Minh-Tri Pham
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  2. Oliver J. Woodford
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  3. Frank Perbet
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  4. Atsuto Maki
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  5. Riccardo Gherardi
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  6. Björn Stenger
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  7. Roberto Cipolla
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Corresponding author

Correspondence to Minh-Tri Pham .

Editor information

Editors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

    Roberto Cipolla

  2. Dipartimento di Matematica e Informatica, Università di Catania, Viale Andrea Doria 6, Catania, 95125, Catania, Italy

    Sebastiano Battiato

  3. Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, Catania, 95125, Italy

    Giovanni Maria Farinella

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Pham, MT. et al. (2013). Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28661-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-28661-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28660-5

  • Online ISBN: 978-3-642-28661-2

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