基于多层次语义特征的建筑立面点云提取方法

An extraction method for building facade point cloud based on multi-level semantic features

建筑立面信息是指建筑物与外部空间接触面的空间分布及属性信息,如何从点云数据中提取建筑立面信息是点云数据处理中的热点和难点。为解决传统格网密度算法在建筑立面点云提取时评价标准单一、适应性不强的问题,综合分析建筑区各类典型地物点云的高程分布、投影密度、法向量分布等局部及整体空间特征,构建由点云单点语义、格网语义及区域语义组成的多层次语义特征描述子,在此基础上提出一种建筑立面点云提取方法,针对建筑立面点云在不同层次语义上的特点设置合理阈值,通过逐层筛选实现建筑立面点云的精确提取。试验结果表明:该算法能在低层、高层以及超高层建筑区等不同场景海量点云中快速准确地实现建筑立面点云提取,算法精度、效率、适应性良好。

Building facade information refers to the spatial distribution and attribute information of the contact surface between buildings and external space.How to extract building facade information from point cloud data is a hot and difficult problem in point cloud data processing.In order to solve the problems of single evaluation standard and weak adaptability of traditional grid density algorithm in building facade point cloud extraction,this paper analyzed the local and overall spatial characteristics such as elevation distribution,projection density and normal vector distribution of various typical surface feature point clouds in the construction area,and constructed a multi-level semantic feature descriptor composed of point cloud single point semantics,grid semantics and regional semantics.Based on this descriptor and the reasonable threshold which was set according to the semantic characteristics of building facade point cloud at different levels,a multi-level semantic feature extraction method was proposed to extract the building facade point cloud accurately layer by layer.The experimental results show that this algorithm can be used to quickly and accurately extract the building facades of low,high buildings and super high buildings from point clouds.Overall,this algorithm achieves a high precision,a high efficiency and a good adaptability.