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An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

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Advances in Visual Computing (ISVC 2016)

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Abstract

This paper introduces a new method for the automated segmentation of laser scanning data for decorative urban buildings. The method combines octree indexing and RANSAC - two previously established but heretofore not integrated techniques. The approach was successfully applied to terrestrial point clouds of the facades of five highly decorative urban structures for which existing approaches could not provide an automated pipeline. The segmentation technique was relatively efficient and wholly scalable requiring only 1 s per 1,000 points, regardless of the façade’s level of ornamentation or non-recti-linearity. While the technique struggled with shallow protrusions, its ability to process a wide range of building types and opening shapes with data densities as low as 400 pts/m2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.

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Acknowledgments

This work was generously funded by the European Research Council grant ERC-2012-StG-20111012 ‘RETURN-Rethinking Tunnelling in Urban Neighbourhoods’ Project 307836. The authors gratefully thank Donal Lennon for the pre-processing of the data sets, as well as for assistance with data acquisition.

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Authors and Affiliations

  1. School of Civil Engineering and Earth Institute, University College Dublin, Dublin, Ireland

    Fatemeh Hamid-Lakzaeian & Debra F. Laefer

Authors
  1. Fatemeh Hamid-Lakzaeian
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  2. Debra F. Laefer
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Corresponding author

Correspondence to Debra F. Laefer .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Hamid-Lakzaeian, F., Laefer, D.F. (2016). An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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