ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume II-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 1–8, 2014
https://doi.org/10.5194/isprsannals-II-3-1-2014
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 1–8, 2014
https://doi.org/10.5194/isprsannals-II-3-1-2014

  07 Aug 2014

07 Aug 2014

Automatic Detection and Feature Estimation of Windows for Refining Building Facades in 3D Urban Point Clouds

A.K Aijazi1,2, P. Checchin1,2, and L. Trassoudaine1,2 A.K Aijazi et al.
  • 1INSTITUT PASCAL, Université Blaise Pascal, Clermont Université, BP 10448, 63000 Clermont-Ferrand, France
  • 2INSTITUT PASCAL, CNRS, UMR 6602, 63171 Aubière, France

Keywords: 3D LiDAR point clouds, Automatic window detection, 3D urban cartography

Abstract. This paper presents a method that automatically detects windows of different shapes, 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/parameters are then estimated exploiting both symmetrically corresponding windows in the façade as well as temporally corresponding windows in successive passages, based on analysis of variance 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 as well as the technical prowess of the method.