Scholarlink

<p>The development of accurate historical building information models provides an indispensable data infrastructure for the comprehensive protection strategy of ancient buildings. This article proposes an automatic reconstruction method for parameterized models of historical building components based on point clouds, which is used for historical building components with horizontal arc layout paths and rounded rectangular cross-sections. Adopting the Moving Least Squares (MLS) method to unsampled the component point cloud, improving the density and integrity of the cross-sectional point cloud. By combining the Random Sample Consistency (RANSAC) algorithm with the Least Squares (LS) algorithm, the geometric parameters of the layout path and cross-section are extracted and parameterized for model reconstruction. Finally, a deviation analysis based on point cloud is conducted to verify the accuracy of the reconstructed model. Through simulation data verification, this technical solution can achieve high-precision extraction of geometric parameters of heritage building components, significantly improving the accuracy of 3D modeling and data processing efficiency.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Parametric fine model reconstruction of ancient architectural layout components based on random sampling consistency algorithm and least squares method—simulation data verification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

查看全文
  • Changlei Jiang

摘要

The development of accurate historical building information models provides an indispensable data infrastructure for the comprehensive protection strategy of ancient buildings. This article proposes an automatic reconstruction method for parameterized models of historical building components based on point clouds, which is used for historical building components with horizontal arc layout paths and rounded rectangular cross-sections. Adopting the Moving Least Squares (MLS) method to unsampled the component point cloud, improving the density and integrity of the cross-sectional point cloud. By combining the Random Sample Consistency (RANSAC) algorithm with the Least Squares (LS) algorithm, the geometric parameters of the layout path and cross-section are extracted and parameterized for model reconstruction. Finally, a deviation analysis based on point cloud is conducted to verify the accuracy of the reconstructed model. Through simulation data verification, this technical solution can achieve high-precision extraction of geometric parameters of heritage building components, significantly improving the accuracy of 3D modeling and data processing efficiency.