通过使用PointNet+实现地铁站空间的模块化智能设计方法
Peng Liu1,2,3, Longlong Zhang1,2, Youman Wei4
1Shaanxi Provincial Land Engineering Construction Group Co., Ltd, Xi'an, 710000, Shaanxi, China.
Scientific reports
|October 6, 2025
概括
本研究将PointNet++的深度学习应用于模块化建筑设计的3D地铁站数据. 该模型有效地识别和分类空间,实现高效和准确的设计优化.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建筑设计 建筑设计
背景情况:
- 地铁站布局表现出相似性和可复制性,使它们适合智能模块化设计.
- 传统的设计方法可能无法充分利用3D空间数据的潜力来优化功能布局.
- PointNet++是一个深度学习架构,有效用于对象识别和3D点云中的语义细分.
研究的目的:
- 用3D空间数据开发和评估地铁站功能空间的基于深度学习的模块化设计方法.
- 评估PointNet++网络在识别和分类地铁站环境中的建筑元素方面的有效性.
- 证明拟议方法对高效准确地识别和分类建筑空间的能力.
主要方法:
- 从多个城市的地铁站收集了3D飞机数据.
- 构建3D模型和导出点云数据 (X,Y,Z,rgbC),然后进行数据增强.
- 在8:1:1的培训,验证和测试数据集上训练了PointNet++网络.
主要成果:
- PointNet++实现了稳定的训练,平均损失为0.42,训练准确度为0.76.
- 该模型在测试组中表现出强的性能,平均类精度为0.80+和整体精度为0.75+,平均IOU约为60%.
- 预测和地面真相结果之间的高相关性表明模型的自我学习能力和方案空间优化能力.
结论:
- 提出的模块化设计方法,利用3D空间数据的深度学习,高效准确地识别和分类建筑空间.
- PointNet++网络非常适合处理3D地铁站数据,为设计提供有效的信息传输.
- 这种方法在像地铁站这样的复杂建筑环境中的功能空间的智能模块化设计中取得了重大进展.
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