利用邻域方向分布的机载激光雷达点云建筑物外轮廓提取

机载激光雷达点云密度分布不均、建筑物形状不规则且复杂多样等多种因素,导致现有建筑物轮廓提取方法存在参数难以设置、适应性较差等问题,为此本文提出一种利用邻域方向分布的机载激光雷达点云建筑物外轮廓提取方法。首先采用固定邻域点数分析各点的邻域方向分布,以获取不同邻域方向间的夹角,根据建筑物外轮廓点的特点,定义潜在轮廓点并将其视为初始轮廓点进行提取,从而得到初始轮廓点提取结果;考虑到无序轮廓点难以用于实际任务,因此利用建筑物点云构建不规则三角网并对其中的边进行删除、添加等操作,得到仅与初始轮廓点相连的边集,通过非固定边长的扫描方式跟踪轮廓点,并基于设计的规则剔除导致明显锯齿状的轮廓点,从而得到有序、平滑的建筑物外轮廓提取结果。利用具有不同密度分布、不同形状的模拟和真实建筑物点云进行试验,结果表明,方法对不同场景采用相同的参数,均能得到较好的外轮廓提取结果,F1分数优于90.88%。方法在保证提取轮廓F1分数较高的同时,可有效地克服参数难以设置的问题,具有较强的适应性,可为建筑物三维模型重建等应用提供稳定、可靠的建筑物外轮廓信息。

Building outer boundary extraction from ALS point clouds using neighbor point direction distribution

Factors such as uneven point density and various irregular and complex shapes of buildings are common in airborne laser scanning point clouds and cause difficulty in setting parameters and low adaptability of existing building-outline-extraction methods.To address these problems,a novel building-outerboundary-extraction method using a neighbor point direction distribution is proposed in this study.First,a specific number of neighbor points are used to analyze the neighborhood point direction distribution to obtain the angles between different directions.The concept of a potential boundary point is defined,all potential boundary points are regarded as boundary points based on the characteristics of the building outlines,and the initial building boundary points are retrieved.Because disorder boundary points are difficult to use in actual tasks,a triangulated irregular network is constructed to obtain the edges between points,and processes such as deletion and addition are performed on the edges to acquire edges that link only boundary points.After the deletion of points that cause obvious zigzag,ordered and smooth building boundaries are finally retrieved by point tracing based on unfixed-length edge scanning.Multi-group of simulated and realscene building point clouds with different point density distributions and shapes are employed in the experiment.The results show that the proposed method can achieve good boundary extraction when the same parameter is set in different scenes.In addition,the F1 scores are found to be higher than 90.88%.The proposed method can ensure high F1 scores of extracted boundaries while effectively overcoming the difficulties in setting parameters and low adaptability.The method can provide stable and reliable building outline information for applications such as three-dimensional building model reconstruction.