Scholarlink

<p>Despite advances in automated Building Information Modeling (BIM) reconstruction, existing methods remain unsuitable for complex historic buildings. This paper proposes a semi-automated reconstruction workflow that integrates the Scan-to-BIM workflow with family library modeling. The method combines region-growing and building semantics for point cloud segmentation, extracts modeling information from component characteristics, constructs parametric families through typological classification of complex elements, automatically filters door and window FamilySymbols via Revit secondary development, and assembles all components into a BIM model. Experiments show that the reconstructed model achieves a root mean square error (RMSE) of 0.039 m. When tolerance threshold τ = 0.25 m, completeness is 94.9% and correctness is 90.9%, demonstrating the reliability of the approach. The method improves automation in historic building documentation and enhances conventional Historic Building Information Modeling (HBIM) workflows. A limitation of this study is its single-case basis, which calls for additional validation across diverse heritage contexts.</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

Semi-automated reconstruction of HBIM from laser scan point clouds by integrating Scan-to-BIM and family library modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

查看全文
  • Qian Wu,
  • Qiang Fu,
  • Pengju Zhang

摘要

Despite advances in automated Building Information Modeling (BIM) reconstruction, existing methods remain unsuitable for complex historic buildings. This paper proposes a semi-automated reconstruction workflow that integrates the Scan-to-BIM workflow with family library modeling. The method combines region-growing and building semantics for point cloud segmentation, extracts modeling information from component characteristics, constructs parametric families through typological classification of complex elements, automatically filters door and window FamilySymbols via Revit secondary development, and assembles all components into a BIM model. Experiments show that the reconstructed model achieves a root mean square error (RMSE) of 0.039 m. When tolerance threshold τ = 0.25 m, completeness is 94.9% and correctness is 90.9%, demonstrating the reliability of the approach. The method improves automation in historic building documentation and enhances conventional Historic Building Information Modeling (HBIM) workflows. A limitation of this study is its single-case basis, which calls for additional validation across diverse heritage contexts.