摘要
本文以机载LiDAR点云数据为研究对象,提出一套建筑物三维模型重建方法。首先使用渐进三角网滤波算法分类地面点与非地面点,通过训练完成的随机森林模型完成建筑物点云提取;其次将方向作为约束条件,使用随机抽样一致(Random Sample Consensus, RANSAC)算法完成建筑物轮廓线提取并获取屋顶关键点信息;最后使用SharpGL工具包,以建筑物轮廓线与屋顶关键点信息为框架重建建筑物三维模型。以实测机载LiDAR点云数据为例进行实验,结果表明本文方法能够提取得到完整的建筑物轮廓信息,并具有较高的建筑物模型重建精度。
Based on the airborne LiDAR point cloud data, this paper proposes a set of building 3D model re-construction methods. Firstly, the gradual triangulation filtering algorithm is used to classify ground points and non-ground points, and the building point cloud is extracted through the trained random forest model;secondly, the direction is taken as the constraint condition, and the random sample consistent (RANSAC) algorithm is used to extract the building contour line and obtain the roof key point information;finally, the SharpGL toolkit is used to reconstruct the 3D model of the building based on the building outline and roof key point information. Taking the measured air-borne LiDAR point cloud data as an example, the experimental results show that the proposed method can extract complete building contour information, and has high building model recon-struction accuracy.
出处
《测绘科学技术》
2023年第3期200-206,共7页
Geomatics Science and Technology
