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基于显著几何特征的古木建筑关键构件 多 LoD 尺寸信息提取方法

  

  1. (1. 北京未来城市设计高精尖创新中心，北京 100044； 
    2. 北京市建筑遗产精细重构与健康监测重点实验室，北京 100044； 
    3. 北京建筑大学测绘与城市空间信息学院，北京 100044； 
    4. 北京建筑大学土木与交通工程学院，北京 100044)
  • 出版日期:2019-08-31 发布日期:2019-08-30
  • 基金资助:
    北京未来城市设计高精尖创新中心项目(UDC2016030200)；北京市属高校高水平教师队伍建设支持计划项目(IDHT20170508)；北京市属 高校高水平教师队伍建设支持计划长城学者培养计划项目(CIT&TCD20180322)

An Extraction Method of Multi-LoD Dimension Information for  the Key Components of Ancient Wooden Architecture  Based on Salient Geometric Features

  1. (1. Beijing Advanced Innovation Center for Future Urban Design, Beijing 100044, China;  
    2. Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, Beijing 100044, China; 
    3. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 
    4. School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)
  • Online:2019-08-31 Published:2019-08-30

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摘要： 摘 要：古木建筑关键构件的尺寸信息是其安全性能评估与提升、历史文化传承的重要基 础，然而该信息的提取尚缺乏高效、高精度的方法。三维激光扫描的精细测绘技术为该问题的 解决提供了科学手段，但点云数据体量庞大，无法直接获取尺寸信息。针对尺寸信息的多细节 层级(LoD)特性，提出了多 LoD 模型标准，建议了相应的显著几何特征参数。结合高保真点云 数据，系统地提出了一套基于点云数据的关键构件多 LoD 尺寸信息自动化提取方法，可准确、 高效地提取多 LoD 尺寸信息。对典型关键构件进行了多 LoD 尺寸信息提取，该方法可在 7 min 内完成百万级点云数据的信息提取，且尺寸相对误差不超过 2%，绝对误差绝大部分小于 0.5 mm，验证了该方法的高效性和可靠性。

关键词: 关 键 词：古木建筑关键构件, LoD 模型, 显著几何特征, 点云数据, 自动化提取方法

Abstract: Abstract: For the assessment and improvement of the safety performance as well as historical and cultural inheritance of ancient wooden architecture, the dimension information of various key components of such architecture acts as the important foundation. However, an extraction method for such dimension information with high efficiency and accuracy is rarely reported. It is well acknowledged that the three dimensional (3D) laser scanning technology has the potential to provide a scientific solution for this problem. However, the point cloud data obtained by 3D laser scanning technology is usually enormous, and the dimension information herein cannot be directly obtained from this data. According to the important characteristics of the key component (i.e. multi-level of details (multi-LoD)) in ancient wooden architecture, a preliminary framework of multi-LoD models is proposed for various types of key components in the ancient wooden architecture, and the correspondingly salient geometric feature parameters, which aim to represent the dimension information of key components, are also recommended according to different LoD. Based on these multi-LoD models and massive high-fidelity point cloud data, an automatic extraction method of multi-LoD dimension information for the key components in ancient wooden architecture is proposed. This method is considered to be capable of accurately and efficiently extracting the multi-LoD dimension information of key components. To validate the reliability and high efficiency of this method, multi-LoD dimension information of two typical key components are extracted using the proposed method. The results indicate that this method is capable of extracting dimension information from millions of point cloud data within 7 minutes. Furthermore, the relative and absolute errors of such information are less than 2% and 0.5 mm respectively, thus validating the high efficiency and reliability of the proposed method.

Key words: Keywords: key components of ancient wooden architecture, LoD model, salient geometric features, point cloud data, automatic extraction method