高效的深度学习实现了传统中国屋顶结构的参数化和3D重建
Ruisi Ou1, Fan Yang1,2, Lili Li1,3
1Nantong Key Laboratory of Spatial Information Technology R&D and Application, College of Geographic Science, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了TCRSym-Net,这是一种用于检测传统中国屋顶对称性的深度学习模型. 这种方法简化了古代中国建筑的复杂建模,用于数字重建.
科学领域:
- 数字遗产是数字遗产的一部分.
- 计算机视觉 计算机视觉
- 建筑工程 建筑工程 建筑工程
背景情况:
- 古代中国建筑具有文化意义,以独特的对称设计为特征.
- 现代技术,如无人机摄影计和激光扫描,有助于复制古老结构,用于遗产建筑信息建模 (HBIM).
- 建模传统的中国屋顶是具有挑战性的,因为复杂的曲面和颠倒的天花板.
研究的目的:
- 开发一种有效的对称性检测和参数提取方法,用于传统的中国屋顶.
- 为了减少为这些结构创建准确的HBIM模型的复杂性和难度.
- 使用镜像复制方法,利用对称性进行快速的数字重建.
主要方法:
- 开发了一个深度学习网络,TCRSym-Net,用于在屋顶点云中识别对称性.
- 处理点云以获得横截面和纵截面.
- 参数建模脚本在Dynamo中编码,使用曲线升高和固体布尔运算.
主要成果:
- TCRSym-Net在准确检测传统中国屋顶点云中的对称性方面表现出有效性.
- 使用拟议的方法,成功地重建了五种不同类型的传统中国屋顶.
- 该研究证实了对称性检测和建模方法的可靠性.
结论:
- 拟议的深度学习方法显著提高了传统中国屋顶对称性检测的准确性和效率.
- 这种方法有助于创建详细可靠的中国古代建筑的HBIM模型.
- 这些发现支持使用人工智能驱动的技术来保护和重建文化遗产.
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