Building Boundary Extraction from LiDAR Point Cloud Data

E. Dey, M. Awrangjeb, F. T. Kurdi, Bela Stantic
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引用次数: 0

Abstract

Building boundary extraction from LiDAR point cloud data is important for urban planning and 3D modelling. Due to the uneven point distribution, missing data, and occlusion in LiDAR point cloud data, extraction of boundary points is challenging. Existing approaches have shortcomings either in detecting boundary points on concave shapes or separate identification of ‘hole’ boundary points inside the building roof. This paper, presents a method for detecting both inner and outer boundary points of the extracted building point cloud. Based on the properties of Delaunay Triangulation and distance from the mean point of the calculated neighbourhood for any point, we extract both inner and outer boundary points. Experimental results using some synthetic shapes as well as some real datasets show the competitive performance of the proposed method.
基于LiDAR点云数据的建筑边界提取
从激光雷达点云数据中提取建筑物边界对于城市规划和三维建模具有重要意义。由于激光雷达点云数据中存在点分布不均匀、数据缺失和遮挡等问题,边界点的提取具有一定的挑战性。现有的方法要么无法检测凹面上的边界点,要么无法单独识别建筑屋顶内部的“洞”边界点。本文提出了一种对提取的建筑物点云的内外边界点进行检测的方法。基于Delaunay三角剖分的性质和任意点与计算邻域均值点的距离,我们提取了内外边界点。在一些合成形状和一些真实数据集上的实验结果表明,该方法具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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