Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 97-103, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W10-97-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018

A SLIDING WINDOW METHOD FOR DETECTING CORNERS OF OPENINGS FROM TERRESTRIAL LIDAR DATA

J. Li1, B. Xiong2,3, F. Biljecki4, and G. Schrotter1,5 J. Li et al.
  • 1ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore
  • 2School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, P.R. China
  • 3Dipper 3D, the Netherlands
  • 4Department of Architecture, National University of Singapore, Singapore
  • 5Geomatics Engineering (GeoZ) City of Zurich, Switzerland

Keywords: Sliding Window, Corners Detection, Openings Detection, LiDAR, Point Clouds, LoD3 Modelling

Abstract. Architectural building models (LoD3) consist of detailed wall and roof structures including openings, such as doors and windows. Openings are usually identified through corner and edge detection, based on terrestrial LiDAR point clouds. However, singular boundary points are mostly detected by analysing their neighbourhoods within a small search area, which is highly sensitive to noise. In this paper, we present a global-wide sliding window method on a projected façade to reduce the influence of noise. We formulate the gradient of point density for the sliding window to inspect the change of façade elements. With derived symmetry information from statistical analysis, border lines of the changes are extracted and intersected generating corner points of openings. We demonstrate the performance of the proposed approach on the static and mobile terrestrial LiDAR data with inhomogeneous point density. The algorithm detects the corners of repetitive and neatly arranged openings and also recovers angular points within slightly missing data areas. In the future we will extend the algorithm to detect disordered openings and assist to façade modelling, semantic labelling and procedural modelling.

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