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Automatic detection and feature estimation of windows in 3D urban point clouds exploiting façade symmetry and temporal correspondences

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Due to the ever increasing demand for more realistic three-dimensional (3D) urban models coupled with recent advancements in ground-based light detection and ranging (lidar) technologies, recovering details of building façade structures, such as windows, has gained considerable attention. However, fewer laser points are usually available for windows as window frames occupy only small parts of building façades while window glass also offers limited reflectivity. This insufficient raw laser information makes it very difficult to detect and recover reliable geometry of windows without human interaction. So, in this article, we present a new 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 two-dimensional (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 and 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 not only the efficacy (with standard accuracy ) but also the technical edge of the proposed method.
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Document Type: Research Article

Affiliations: Université Blaise Pascal, Clermont Université BP 10448, F-63000, Clermont-Ferrand, France

Publication date: November 17, 2014

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