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H-BIM 与人工智能：建筑遗产的半自动扫描到 BIM 重建分类。

H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction.

机构信息

Department of Energy, Systems, Land and Construction Engineering (DESTEC), University of Pisa, 56122 Pisa, Italy.

Civil and Industrial Engineering, ASTRO Laboratory, University of Pisa, 56122 Pisa, Italy.

出版信息

Sensors (Basel). 2023 Feb 23;23(5):2497. doi: 10.3390/s23052497.

DOI:10.3390/s23052497
PMID:36904701
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10007271/
Abstract

We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage- or Historic-Building Information Modeling (H-BIM) reconstruction from laser scanning or photogrammetric surveys is a manual, time-consuming, overly subjective process, but the emergence of AI techniques, applied to the realm of existing architectural heritage, is offering new ways to interpret, process and elaborate raw digital surveying data, as point clouds. The proposed methodological approach for higher-level automation in Scan-to-BIM reconstruction is threaded as follows: (i) semantic segmentation via Random Forest and import of annotated data in 3D modeling environment, broken down class by class; (ii) reconstruction of geometries of classes of architectural elements; (iii) propagation of reconstructed geometries to all elements belonging to a typological class. Visual Programming Languages (VPLs) and reference to architectural treatises are leveraged for the Scan-to-BIM reconstruction. The approach is tested on several significant heritage sites in the Tuscan territory, including charterhouses and museums. The results suggest the replicability of the approach to other case studies, built in different periods, with different construction techniques or under different states of conservation.

摘要

我们提出了一种半自动的扫描到 BIM 重建方法，充分利用人工智能 (AI) 技术，对数字建筑遗产数据进行分类。如今，从激光扫描或摄影测量调查中对遗产或历史建筑信息建模 (H-BIM) 的重建是一个手动的、耗时的、过于主观的过程，但是 AI 技术的出现，应用于现有的建筑遗产领域，为解释、处理和详细说明原始数字测量数据（如点云）提供了新的方法。在扫描到 BIM 重建中实现更高层次自动化的建议方法如下：(i) 通过随机森林进行语义分割，并在 3D 建模环境中导入分类数据；(ii) 重建建筑元素类别的几何形状；(iii) 将重建的几何形状传播到属于典型类别的所有元素。可视化编程语言 (VPL) 和对建筑论著的参考被用于扫描到 BIM 的重建。该方法已在托斯卡纳地区的几个重要遗产地进行了测试，包括寺院和博物馆。结果表明，该方法可以复制到其他案例研究中，这些案例研究是在不同时期、使用不同的施工技术或在不同的保护状态下建造的。

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