由粗到精的机载点云建筑物屋面分割

机载激光雷达技术在城市三维建模中得到越来越广泛的应用,建筑物屋面分割在三维建模中是一项至关重要的环节,提出一种由粗到精的建筑物屋面分割算法。首先使用具有先验知识的低秩子空间聚类框架的法向量估算方法估算每点的法向量及平面方程；再依据法向量一致性以及点到面的距离进行约束实现屋面粗分割；选取粗分割中包含点数最多的一簇点集拟合平面,从未精分割的点集中提取所有满足该平面方程的点集,再将点集投影到该点集拟合的平面上,在投影平面上实现对屋面的精分割。对剩余未分割的点集重复粗分割与精分割步骤,直至将所有屋面提取完毕。使用多种结构不同的建筑物屋面验证本文方法,同时与其他三种算法进行比较,结果表明相比较于其他三种算法,本文算法均能精确、完整地分割出建筑物各屋面,不存在错分割、漏分割的现象。该方法可以精确提取结构不同的建筑物屋面,为后续建筑物建模提供准确的面片。

Building roof planes segmentation of airborne LiDAR by using coarse-to-fine method

Airborne light detection and ranging technology is widely used in the urban 3D reconstruction in recent years,and building roof planes segmentation is a basic and crucial step in the automatic reconstruction.In this paper a coarse-to-fine method was proposed to segment building roof planes automatically.Firstly,the normal and planar equations of each point were calculated via the method of the low-rank subspace clustering framework with prior knowledge(LRSCPK).Secondly,the buildings were coarsely segmented based on the normal vector consistency and the distance from the point to the plane.Thirdly,the planar equation of the cluster with the largest number of points in the coarse segmentation was estimated,and the points satisfying the planar equation were extracted from the points without fine segmentation.Then the extracted points of the clustering with the maximum number of points were projected on the fitting planes,on which the plane was segmented finely.Lastly,the rest roof planes were extracted by repeating the coarse and fine steps above.The method was validated by using several buildings with different structures,and segmented results were compared with other three algorithms.The results showed that compared with other three algorithms,the building roof planes were accurately segmented by using the proposed method,without spurious roof planes.The method was able to accurately segment the roof of buildings with different structures and provide accurate planes for 3D reconstruction.