Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

IF 2.6 1区艺术学 Q2 CHEMISTRY, ANALYTICAL Heritage Science Pub Date : 2024-02-21 DOI:10.1186/s40494-024-01179-4

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Abstract

This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

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基于深度学习的从语义点云到语义 BIM 模型的遗产数字孪生方法

摘要本研究的重点是应用深度学习将语义点云转换为语义建筑信息模型（BIM），以创建遗产数字孪生体，其中心是中国四川具有历史和文化意义的桃坪村。利用无人机和地面激光扫描等先进技术，我们捕捉到了该村的详细点云数据。KP-SG 神经网络是我们研究方法中的一个关键要素，它表现出了卓越的整体性能，尤其擅长于准确识别 11 个类别。其中，建筑物和植被的识别率分别达到 81% 和 83%，与 KP-FCNN 相比，mIoU 提高了 2.53%。这一准确率对于构建详细、准确的桃坪村语义 BIM 模型至关重要，有助于进行全面的建筑和景观分析。此外，KP-SG 卓越的分割能力有助于创建高保真三维模型，丰富虚拟现实体验。我们还介绍了一个数字孪生平台，该平台整合了各种数据集、其语义信息和可视化工具。该平台旨在支持流程自动化和决策，并为游客提供身临其境的体验。我们的方法整合了语义 BIM 模型和数字孪生平台，标志着在保护和理解像桃坪这样的传统村落方面取得了重大进展，并展示了深度学习在文化遗产保护方面的变革潜力。

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来源期刊

Heritage Science Arts and Humanities-Conservation

CiteScore

4.00

自引率

20.00%

发文量

183

审稿时长

19 weeks

期刊介绍： Heritage Science is an open access journal publishing original peer-reviewed research covering: Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance. Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies. Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers. Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance. Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance. Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects. Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above. Description of novel technologies that can assist in the understanding of cultural heritage.