自动从点云重建管道网络，提升数字孪生建模效率
Pipe Reconstruction from Point Cloud Data
- 用拉普拉斯收缩法提取管道骨架曲线，再通过延展优化
- 结合滚动球与2D圆拟合实现轴线精修，准确恢复半径与方向
- 适合工业设备数字化，尤其适用于船舶与海上平台建模
工业资产（如船舶和海上平台）的精准数字孪生依赖于复杂管道网络的精确重建。然而，从激光扫描数据中手动建模管道耗时且劳动密集。本文提出一种自动化管道重建流程，首先利用基于拉普拉斯的收缩方法估计骨架曲线，再进行曲线延展；随后采用滚动球技术结合二维圆拟合对骨架轴线重新定位，并通过三维平滑进一步优化。该方法可准确获取管道的半径、长度与空间朝向，支持复杂管道网络的高精度三维建模。通过自动化处理，显著提升建模速度与准确性，降低开发成本，助力数字孪生系统构建。
原文摘要 · Abstract (English)
Accurate digital twins of industrial assets, such as ships and offshore platforms, rely on the precise reconstruction of complex pipe networks. However, manual modelling of pipes from laser scan data is a time-consuming and labor-intensive process. This paper presents a pipeline for automated pipe reconstruction from incomplete laser scan data. The approach estimates a skeleton curve using Laplacian-based contraction, followed by curve elongation. The skeleton axis is then recentred using a rolling sphere technique combined with 2D circle fitting, and refined with a 3D smoothing step. This enables the determination of pipe properties, including radius, length and orientation, and facilitates the creation of detailed 3D models of complex pipe networks. By automating pipe reconstruction, this approach supports the development of digital twins, allowing for rapid and accurate modeling while reducing costs.
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