Pipe Reconstruction from Point Cloud Data

TL;DR

This paper introduces an automated pipeline for reconstructing complex pipe networks from incomplete laser scan data, significantly improving efficiency and accuracy in creating digital twins of industrial assets.

Contribution

The proposed method automates pipe reconstruction using a novel skeleton estimation and refinement process, reducing manual effort and enhancing model precision.

Findings

Automated pipeline accurately reconstructs pipe networks from laser scans.

Reduces manual modeling time and labor costs.

Enables rapid creation of detailed digital twins.

Abstract

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.