Please use this identifier to cite or link to this item: https://doi.org/10.5194/isprs-archives-XLII-4-W10-97-2018
Title: A sliding window method for detecting corners of openings from terrestrial lidar data
Authors: Jiaqiang Li
Biao Xiong
Filip Biljecki 
Gerhard Schrotter
Keywords: Sliding Window
Corners Detection
Openings Detection
LiDAR
Point Clouds
LoD3 Modelling
Issue Date: 12-Sep-2018
Citation: Jiaqiang Li, Biao Xiong, Filip Biljecki, Gerhard Schrotter (2018-09-12). A sliding window method for detecting corners of openings from terrestrial lidar data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-4/W10 : 97-103. ScholarBank@NUS Repository. https://doi.org/10.5194/isprs-archives-XLII-4-W10-97-2018
Rights: Attribution 4.0 International
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.
Source Title: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
URI: http://scholarbank.nus.edu.sg/handle/10635/147304
ISSN: 1682-1750
DOI: 10.5194/isprs-archives-XLII-4-W10-97-2018
Rights: Attribution 4.0 International
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