Pipe Reconstruction from Point Cloud Data
This addresses the time-consuming and costly process of creating digital twins for industrial assets like ships and offshore platforms, though it appears incremental as it builds on existing techniques like Laplacian-based contraction and circle fitting.
The paper tackles the problem of manually modeling pipes from laser scan data by presenting an automated pipeline for pipe reconstruction from incomplete data, enabling the determination of pipe properties like radius and length to create detailed 3D models.
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.