利用密集匹配点云的建筑单体提取算法研究

Single Part of Building Extraction from Dense Matching Point Cloud

以三维点云或模型表达的单体化建筑信息是城市规划、市政管理、数字城市建设等多个应用领域的关键信息要素。利用航空影像密集匹配点云,提出了一种针对复杂建筑区域建筑单体的快速提取算法。在对点云进行滤波处理及水平点云提取和聚类的基础上,将点云面域投影至二维平面格网化,并结合立面信息及面域几何特征将非屋顶面的点云面域滤除,进一步基于栅格图像计算点云面域之间的拓扑关系,得到了各建筑单体的点云覆盖范围,最后实现了建筑单体点云的提取。实验结果表明,所提算法对建筑单体点云提取的召回率和查准率平均值分别为92.6%和89.9%,说明所提算法能够有效支撑复杂区域建筑单体的提取。

Single part information of building represented by three-dimensional point cloud or model representation is a key information factor in numbers of applications,such as urban planning,municipal management and digital city construction.Using dense matching point cloud generated by aerial images,we propose a new algorithm for rapidly single part of building extraction in complex construction area. On the basic of ground filtering and clustering after horizontal point cloud extraction,the algorithm projects all the point cloud clusters into the two dimensional grid. Non-roof segments are removed based on building fa9 ade and clusters’ geometrical characteristic. Then,topological relationships between clusters computed based on grid images are adopted to generate the range of single part of the building. And the single part point clouds are extracted finally. Experimental results show that the average recall and the average precision of single part of building extraction are 92. 6% and 89. 9%,and it means that it is efficient for our algorithm to extract single part of building in complex urban area.