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Surface Recovery from 3D Point Data Using a Combined Parametric and Geometric Flow Approach

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2683))

Abstract

This paper presents a novel method for surface recovery from discrete 3D point data sets. In order to produce improved reconstruction results, the algorithm presented in this paper combines the advantages of a parametric approach to model local surface structure, with the generality and the topological adaptability of a geometric flow approach. This hybrid method is specifically designed to preserve discontinuities in 3D, to be robust to noise, and to reconstruct objects with arbitrary topologies. The key ideas are to tailor a curvature consistency algorithm to the case of a set of points in 3D and to then incorporate a flux maximizing geometric flow for surface reconstruction. The approach is illustrated with experimental results on a variety of data sets.

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Author information

Authors and Affiliations

  1. Centre for Intelligent Machines, McGill University, 3480 University Street, Montréal, Québec, H3A 2A7, Canada

    Peter Savadjiev, Frank P. Ferrie & Kaleem Siddiqi

Authors
  1. Peter Savadjiev
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  2. Frank P. Ferrie
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  3. Kaleem Siddiqi
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. Instituto de Telecomunicações, Instituto Superior Técnico, T.U. Lisbon, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  3. ARIANA Research Group, INRIA/I3S, Sophia Antipolis, France

    Josiane Zerubia

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© 2003 Springer-Verlag Berlin Heidelberg

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Savadjiev, P., Ferrie, F.P., Siddiqi, K. (2003). Surface Recovery from 3D Point Data Using a Combined Parametric and Geometric Flow Approach. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_21

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  • DOI: https://doi.org/10.1007/978-3-540-45063-4_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40498-9

  • Online ISBN: 978-3-540-45063-4

  • eBook Packages: Springer Book Archive

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