一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法

从LiDAR数据中高精度地提取建筑物屋顶面是构建屋顶面拓扑关系、实现建筑物三维模型重建的关键。本文针对现有算法提取复杂建筑物屋顶面适应性较差、精度较低等问题,提出了一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法。通过主成分分析计算点云特征,构建特征直方图,选取可靠种子点;利用提出的局部点云法向量分布密度聚类算法聚类种子点,快速准确地提取初始屋顶面片;构建基于邻域信息的投票模型,有效地解决屋顶面竞争现象。试验结果表明,本文方法可自动、高精度地提取屋顶面,对不同复杂程度的建筑物具有较好的适应性,能为建筑物三维模型重建提供可靠的屋顶面信息。

Accurate and Automatic Building Roof Extraction Using Neighborhood Information of Point Clouds

High accuracy building roof extraction from LiDAR data is the key to build topological rel ationship of building roofs and reconstruct buildings .Aiming at the poor adaptation and low extraction precision of existing roof extraction methods for complex building ,an accurate and automatic building roof extraction method using neighborhood information of point clouds is proposed .Point clouds features are calculated by principle component analysis ,and reliable seed points are selected after feature histogram construction .Initi al roof surfaces are extracted quickly and precisely by the proposed local normal vector distribution density-based spatial clustering of applications with noise (LNVD-DBSCAN) .Roof competition problem is solved effectively by the poll model based on neighborhood information .Experimental results show that the proposed method can extract building roofs automatically and precisely ,and has preferable adaptation to buildings with different complexity ,which is able to provide reliable roof information for building reconstruction .