测绘学报 ›› 2017, Vol. 46 ›› Issue (10): 1460-1469.doi: 10.11947/j.AGCS.2017.20170345

• 摄影测量学与遥感 • 上一篇    下一篇

点云信息提取研究进展和展望

张继贤1, 林祥国2, 梁欣廉3   

  1. 1. 国家测绘产品质量检验测试中心, 北京 100830;
    2. 中国测绘科学研究院, 北京 100830;
    3. 芬兰地理信息研究所, 芬兰 基尔科努米 02431
  • 收稿日期:2017-06-22 修回日期:2017-09-07 出版日期:2017-10-20 发布日期:2017-10-26
  • 通讯作者: 林祥国 E-mail:linxiangguo@casm.ac.cn
  • 作者简介:张继贤(1965-),男,研究员,博士生导师,研究方向为资源与环境遥感监测。E-mail:zhangjx@casm.ac.cn
  • 基金资助:
    国家自然科学基金(41671440;41371405);遥感青年科技人才创新资助计划

Advances and Prospects of Information Extraction from Point Clouds

ZHANG Jixian1, LIN Xiangguo2, LIANG Xinlian3   

  1. 1. National Quality Inspection and Testing Center for Surveying and Mapping Products, Beijing 100830, China;
    2. Chinese Academy of Surveying and Mapping, Beijing 100830, China;
    3. Finnish Geospatial Research Institute, Kirkkonummi 02431, Finland
  • Received:2017-06-22 Revised:2017-09-07 Online:2017-10-20 Published:2017-10-26
  • Supported by:
    The National Natural Science Foundations of China (Nos. 41671440;41371405);The Foundation for Remote Sensing Young Talents by the National Remote Sensing Center of China

摘要: 点云是目前摄影测量、遥感、计算机视觉等多个领域广泛应用的数据源之一,而信息提取是点云处理、分析和应用的必经环节。为此,学术界已经提出了大量点云信息提取方法。本文从基元类型、提取特征、特征选择与分类器等3个视角概括了点云信息提取的相关研究现状,总结出点云信息提取存在的5个主要问题,点明了点云信息提取的6个主要发展趋势,并着重介绍了“融合多基元的点云信息提取范式”。

关键词: 激光雷达点云, 摄影测量点云, 滤波, 分类, 多基元融合, 信息提取

Abstract: Point cloud is one type of the widely used data sources in many communities such as photogrammetry, remote sensing, and computer vision etc. Moreover, information extraction is a necessary step in the process of point cloud processing, analysis and applications. As result, the scholars have proposed a great number of methods for point cloud information extraction. According to the three view points of primitive types, extracted features, and methods for feature selection and classification, this review paper summarizes the research status of point cloud information extraction. This paper also point out five main problems and six main trends in point cloud information extraction, especially introduces a new paradigm:fusion of multiple primitives for point cloud information extraction.

Key words: LiDAR point cloud, photogrammetric point cloud, filtering, classification, fusion of multiple primitives, information extraction

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