基于区域生长算法的复杂建筑物屋顶点云分割

Point cloud segmentation on the roof of complicated building based on the algorithm of region growing

精确分割建筑物屋顶激光雷达(light detection and ranging,LiDAR)点云是三维模型重建的重要环节。针对现有算法分割复杂建筑物屋顶面结构精度差的问题,提出一种以三角面为基元的基于区域生长算法的复杂建筑物屋顶点云分割方法。首先,构建Delaunay三角网建立各激光点间相互关系,计算各三角面法向量,利用同一建筑物面片上各三角面法向量基本一致的特征对点云进行初步划分;然后,由于点云散乱性及误差影响产生诸多散乱三角面,对各构成散乱三角面的点进行剖分,并基于具有良好鲁棒性的随机采样一致性算法(random sample consensus,RANSAC),结合Alpha Shape算法获取建筑物各面片边界,合并过度分割的面片及孤立点,完成建筑物屋顶点云分割。实验结果表明,该方法对复杂建筑物屋顶点云分割的完整性、正确性及质量均较为理想。

Segmenting light detection and ranging(LiDAR)point cloud of building accurately is the important section in the reconstruction of three-dimensional model.In view of the complex roof structure of complex buildings and poor segmentation accuracy of the existing algorithms,the authors put forward a kind of algorithm of region growing with the basic element of triangles to segment the point cloud of the building.First of all,Delaunay triangulation network is constructed,correlation is set up among laser points,unit normal vectors of triangles are calculated,initial partition is conducted on point cloud with the character that vectors in unit vector approach of triangles on the same plane of the building are basically consistent;then,because dispersion and deviation of point cloud could produce many disheveled triangles,dissection is conducted on points that are composed of disheveled triangles;based on good robustness of random sample consensus(RANSAC)algorithm,boundaries of planes of the building combining are obtained with Alpha Shape algorithm,plane and isolated point are combined in over-segmentation.The test result shows that the point cloud segmentation on the roof of the building is ideal in integrity,accuracy and quality with the method put forward in this paper.