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<p>This study addresses the urgent challenge of digitally preserving severely damaged vernacular architecture that lacks complete historical documentation. Taking the Dan Tao’s Former Residence, a Qing Dynasty dwelling in the Jingchu region, as a case study, we propose a reproducible multimodal framework for reverse restoration. The approach integrates SLAM-based laser scanning, UAV photogrammetry, historical documentary evidence, analogy-driven HBIM construction, and knowledge-graph visualization. By bridging the semantic gaps between material remains, images, and textual records, the workflow enables high-fidelity digital modeling of complex components while decoding cultural features at multiple scales. Results demonstrate that the framework overcomes limitations of insufficient point-cloud density and missing documentation, achieving accurate 3D restoration of degraded structures and establishing a scalable cultural feature recognition system for Jingchu vernacular architecture. This research provides both methodological innovation and practical tools for conservation, offering a transferable paradigm for safeguarding endangered architectural heritage worldwide.</p>

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Digital restoration and feature recognition of a Qing-Dynasty vernacular dwelling based on multimodal data fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月13日

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  • Xiaotang Xia,
  • Zhihan Chen,
  • Yulin Xu

摘要

This study addresses the urgent challenge of digitally preserving severely damaged vernacular architecture that lacks complete historical documentation. Taking the Dan Tao’s Former Residence, a Qing Dynasty dwelling in the Jingchu region, as a case study, we propose a reproducible multimodal framework for reverse restoration. The approach integrates SLAM-based laser scanning, UAV photogrammetry, historical documentary evidence, analogy-driven HBIM construction, and knowledge-graph visualization. By bridging the semantic gaps between material remains, images, and textual records, the workflow enables high-fidelity digital modeling of complex components while decoding cultural features at multiple scales. Results demonstrate that the framework overcomes limitations of insufficient point-cloud density and missing documentation, achieving accurate 3D restoration of degraded structures and establishing a scalable cultural feature recognition system for Jingchu vernacular architecture. This research provides both methodological innovation and practical tools for conservation, offering a transferable paradigm for safeguarding endangered architectural heritage worldwide.