一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法

赵传; 张保明; 陈小卫; 郭海涛; 卢俊

doi:10.11947/j.AGCS.2017.20160518

测绘学报 >

2017 , Vol. 46 >Issue 9: 1123 - 1134

DOI: https://doi.org/10.11947/j.AGCS.2017.20160518

摄影测量学与遥感

一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法

赵传 ,
张保明 ,
陈小卫 ,
郭海涛 ,
卢俊

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1. 信息工程大学地理空间信息学院, 河南郑州 450001;
2. 地理信息工程国家重点实验室, 陕西西安 710054

赵传(1991-),男,博士生,研究方向为点云数据处理、基于点云数据的建筑物三维模型重建。E-mail:zc_mail163@163.com

收稿日期: 2016-10-25

修回日期: 2017-08-09

网络出版日期: 2017-10-12

基金资助

国家自然科学基金（41601507）；地理信息工程国家重点实验室开放基金（SKLGIE2015-M-3-3）

收起

Accurate and Automatic Building Roof Extraction Using Neighborhood Information of Point Clouds

ZHAO Chuan ,
ZHANG Baoming ,
CHEN Xiaowei ,
GUO Haitao ,
LU Jun

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1. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China;
2. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China

Received date: 2016-10-25

Revised date: 2017-08-09

Online published: 2017-10-12

Supported by

The National Natural Science Foundation of China (No. 41601507);The Open Research Foundation of State Key Laboratory of Geo-information Engineering (No. SKLGIE2015-M-3-3)

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摘要

从LiDAR数据中高精度地提取建筑物屋顶面是构建屋顶面拓扑关系、实现建筑物三维模型重建的关键。本文针对现有算法提取复杂建筑物屋顶面适应性较差、精度较低等问题，提出了一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法。通过主成分分析计算点云特征，构建特征直方图，选取可靠种子点；利用提出的局部点云法向量分布密度聚类算法聚类种子点，快速准确地提取初始屋顶面片；构建基于邻域信息的投票模型，有效地解决屋顶面竞争现象。试验结果表明，本文方法可自动、高精度地提取屋顶面，对不同复杂程度的建筑物具有较好的适应性，能为建筑物三维模型重建提供可靠的屋顶面信息。

关键词： 建筑物屋顶面; LiDAR数据; 邻域信息; 密度聚类; 点云; 建筑物三维重建

本文引用格式

赵传 , 张保明 , 陈小卫 , 郭海涛 , 卢俊 . 一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法[J]. 测绘学报, 2017 , 46(9) : 1123 -1134 . DOI: 10.11947/j.AGCS.2017.20160518

Abstract

High accuracy building roof extraction from LiDAR data is the key to build topological relationship of building roofs and reconstruct buildings. Aiming at the poor adaptation and low extraction precision of existing roof extraction methods for complex building, an accurate and automatic building roof extraction method using neighborhood information of point clouds is proposed. Point clouds features are calculated by principle component analysis, and reliable seed points are selected after feature histogram construction. Initial roof surfaces are extracted quickly and precisely by the proposed local normal vector distribution density-based spatial clustering of applications with noise (LNVD-DBSCAN). Roof competition problem is solved effectively by the poll model based on neighborhood information. Experimental results show that the proposed method can extract building roofs automatically and precisely, and has preferable adaptation to buildings with different complexity, which is able to provide reliable roof information for building reconstruction.

Key words： building roofs; LiDAR data; neighborhood information; density-based clustering; point cloud; 3D building reconstruction

参考文献

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