Decoding the Past: A Genetic Algorithm-Based Method for Extracting Decorative Patterns in Heritage Digital Twins

Siyuan Meng, Guangji Xu, Wenjin Zhang and Fan Xue ()
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Siyuan Meng: The University of Hong Kong
Guangji Xu: Anthropology Museum of Guangxi
Wenjin Zhang: Guangzhou Okay Information Technology Ltd
Fan Xue: The University of Hong Kong

Chapter Chapter 140 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 2021-2032 from Springer

Abstract: Abstract In the smart construction era, Heritage Digital Twin (HDT) is increasingly created as the digital replica of physical heritage buildings and relics. Extraction of the unique patterns and decorative elements on the HDTs is not only of academic interest to heritage conservation but also of business interest to fashion and design, such as the recent Hanfu fever. However, the patterns’ complex curvature surfaces and subtle protrusions make it challenging to extract and analyze them accurately and efficiently. This paper presents a Genetic Algorithm-based semi-automatic method for extracting decorative pattern texture from HDTs. This method has three steps: (i) extraction of cross-section contour as Non-uniform rational B-spline (NURBS) curves; (ii) Fitting of arcs and curvature projection based on Genetic Algorithm (GA); and (iii) clustering and extraction of patterns of interest. We tested the method on 3D data of a heritage building and a heritage bronze drum preliminarily. The high accuracy of the results, i.e., F1-value > 90% in all tasks, validated our automated extraction method for detailed patterns and decorations. The proposed GA-based method can enrich the literature of HDT in smart heritage and smart construction, whereas the extracted heritage’s patterns and decorations have the potential for cultural and business applications.

Keywords: Heritage Digital Twin; Heritage decorative patterns; 3D point cloud; Genetic algorithm; Smart construction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-1949-5_141

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