基于体素增长的机载LiDAR点云建筑物屋顶面分割

Segmentation of Building Roofs from Airborne LiDAR Point Clouds Using Voxel-based Region Growing

建筑物屋顶面的准确分割对建筑物模型重建具有重要意义。但是由于建筑物屋顶面的种类繁多、大小差异大、形状复杂等特点,以及机载LiDAR点云数据的密度不均、数据量大等特性,传统分割方法存在欠分割、过分割以及小平面难以准确分割等问题。针对上述问题,本文提出一种基于体素区域增长的机载LiDAR点云建筑物屋顶面分割方法,有效提升了复杂结构建筑物屋顶面的分割精度。首先,对点云数据进行体素化,根据PCA方法估计每个体素的法向量、曲率值。然后实现基于体素区域增长的初始屋顶面分割。该过程选取当前体素空间曲率最小值体素作为初始种子体素,以26邻域为增长方向,根据种子体素与待增长体素间的法向量夹角约束增长。迭代增长过程中根据当前种子体素与增长体素的曲率差绝对值确定待增长种子体素,直到未有新的种子体素出现停止增长。再次选择新的初始种子体素重复该过程,直到完成所有体素的分割。最后通过对初始分割结果中过分割屋顶面进行合并、屋顶面结构完整性修复、复杂建筑物的小平面提取等优化处理得到最终的屋顶面。本文选取ISPRS官网提供的Vaihingen和Toronto 2个地区的机载LiDAR点云数据,分别对其中代表性的单栋建筑物和区域建筑物进行屋顶面分割实验。结果表明,复杂建筑物屋顶面点云分割的完整率、正确率和分割质量结果分别为95.36%~99.58%、94.83%~100%和90.65%~98.28%。本文方法在解决欠分割和过分割问题的基础上,有效地提高了建筑物点云的屋顶面分割的精度。

Accurate segmentation of building roofs is important for 3D building model reconstruction.However,traditional segmentation methods have some problems such as under-segmentation,over-segmentation,and difficulty in accurately segmenting small surfaces due to the diverse and complex characteristics of building roofs such as varying sizes and shapes,as well as the properties of LiDAR point clouds such as uneven density and large amount of data.To address these problems,this paper proposes a building roof segmentation method using voxel-based region growing to improve the segmentation accuracy from airborne LiDAR point clouds.First,the voxel size is determined based on the density of points derived from the point clouds projected onto the xyplane,and the voxelization of the point cloud is performed.Then,the normal vector and curvature of each voxel are estimated using the PCA method.This leads to the initial roof segmentation results based on voxel growing.During the segmentation process,the voxel with the minimum curvature value is selected as the initial seed voxel,and the surrounding 26-neighborhood voxels are assigned as the growing voxels.The growth is constrained by the angle of the normal vector between the seed voxel and the growing voxels.The new seed voxels to be grown are determined iteratively based on the absolute difference of the curvature values between the current seed voxel and the growing voxels.The growth continues until no new seed voxels appear.This process is repeated by selecting a new initial seed voxel until the segmentation of all voxels is completed.Finally,the final roof surface is obtained through optimization processes such as merging the over-segmented roof surface into the initial segmentation results,repairing the integrity of the roof surface,and extracting small surfaces for complicated buildings.In this paper,airborne LiDAR point cloud data from two regions,i.e.,Vaihingen and Toronto,provided by ISPRS official website,are selected to perform roof segmentation experiments of single buildings and building areas.The results show that the completeness,accuracy,and quality of the point cloud segmentation of complicated building roofs are 95.36%~99.58%,94.83%~100%,and 90.65%~98.28%,respectively.The proposed method can effectively improve the accuracy of roof segmentation based on LiDAR point clouds without undersegmentation and over-segmentation problems,which provides reliable basic data for the automatic construction of 3D building models based on LiDAR data.