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

  • Ahmad K. Aijazi
  • Paul Checchin
  • Laurent Trassoudaine
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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.



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ahmad K. Aijazi
    • 1
  • Paul Checchin
    • 1
  • Laurent Trassoudaine
    • 1
  1. 1.Institut Pascal, CNRSUniversité Blaise Pascal, Clermont UniversitéAubi èreFrance

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