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<p>To efficiently and accurately generate ancient architectural components, an algorithm for generating ancient architectural components based on Rhino. This study collects data on ancient architectural components through ground LiDAR field measurements, historical literature mining, and digital model libraries. The data is preprocessed using algorithms such as bilateral filtering and maximum entropy segmentation, and features are extracted using convolutional neural networks. Based on the generative adversarial network framework, targeted improvements are made to its generator and discriminator, incorporating attention mechanisms and size control modules. A multi-scale training strategy and a new loss function are adopted, and Rhino. Inside and Grasshopper are integrated to achieve parameterized design and collaborative generative adversarial network. The results showed that the average peak signal-to-noise ratio of the generated components was 30.2&#xa0;dB, the mean absolute error of beam size was 8.2&#xa0;mm, the cosine similarity of decorative patterns was 0.82, and the average time to generate a single component was only 0.7&#xa0;s. This algorithm outperforms comparative algorithms in visual realism, structural rationality, restoration of historical and cultural features. It has strong applicability to different types of ancient building components, providing strong technical support for the digital protection, restoration, and design of ancient buildings.</p>

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Algorithm for generating ancient architectural components based on Rhino.Inside and Grasshopper

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月11日

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  • Hong Li,
  • Yonghui Shi,
  • Qiwei Li

摘要

To efficiently and accurately generate ancient architectural components, an algorithm for generating ancient architectural components based on Rhino. This study collects data on ancient architectural components through ground LiDAR field measurements, historical literature mining, and digital model libraries. The data is preprocessed using algorithms such as bilateral filtering and maximum entropy segmentation, and features are extracted using convolutional neural networks. Based on the generative adversarial network framework, targeted improvements are made to its generator and discriminator, incorporating attention mechanisms and size control modules. A multi-scale training strategy and a new loss function are adopted, and Rhino. Inside and Grasshopper are integrated to achieve parameterized design and collaborative generative adversarial network. The results showed that the average peak signal-to-noise ratio of the generated components was 30.2 dB, the mean absolute error of beam size was 8.2 mm, the cosine similarity of decorative patterns was 0.82, and the average time to generate a single component was only 0.7 s. This algorithm outperforms comparative algorithms in visual realism, structural rationality, restoration of historical and cultural features. It has strong applicability to different types of ancient building components, providing strong technical support for the digital protection, restoration, and design of ancient buildings.