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Automatic Detection and Feature Estimation of Windows from Mobile Terrestrial LiDAR Data

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Intelligent Autonomous Systems 13

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

This work presents a new method of automatic window detection in 3D LiDAR point clouds obtained from mobile terrestrial data acquisition systems in the urban environment. The proposed method first segments out 3D points belonging to the building façade from the 3D urban point cloud and then projects them onto a 2D plane parallel to the building façade. After point inversion within a watertight boundary, windows are segmented out based on geometrical information. The window features are then estimated exploiting both symmetrically corresponding windows in the façade as well as temporally corresponding windows in successive passages, based on ANOVA measurements. This unique fusion of information not only accommodates for lack of symmetry but also helps complete missing features due to occlusions. The estimated windows are then used to refine the 3D point cloud of the building façade. The results, evaluated on real data using different standard evaluation metrics, demonstrate the efficacy of the method.

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Acknowledgments

This work is supported by the Agence Nationale de la Recherche (ANR—the French national research agency) (ANR CONTINT iSpace&Time—ANR-10-CONT-23) and by “le Conseil Général de l’Allier”.

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Authors and Affiliations

  1. Institut Pascal, CNRS, Université Blaise Pascal, Clermont Université, UMR 6602, 63171, Aubi ère, France

    Ahmad K. Aijazi, Paul Checchin & Laurent Trassoudaine

Authors
  1. Ahmad K. Aijazi
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  2. Paul Checchin
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  3. Laurent Trassoudaine
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Corresponding author

Correspondence to Ahmad K. Aijazi .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Aijazi, A.K., Checchin, P., Trassoudaine, L. (2016). Automatic Detection and Feature Estimation of Windows from Mobile Terrestrial LiDAR Data. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_48

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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