Synthetic Point Cloud Generation for Class Segmentation Applications

TL;DR

This paper introduces a novel synthetic point cloud generation method to facilitate automated class segmentation, aiming to improve digital twin creation for industrial maintenance by reducing data annotation efforts.

Contribution

It presents a new approach to generate synthetic point clouds for class segmentation, addressing the lack of existing synthetic data generation methods in industrial applications.

Findings

Helios++ effectively segments point clouds from 3D models.

Synthetic data can enhance automated segmentation accuracy.

Potential to accelerate digital twin development in industry.

Abstract

Maintenance of industrial facilities is a growing hazard due to the cumbersome process needed to identify infrastructure degradation. Digital Twins have the potential to improve maintenance by monitoring the continuous digital representation of infrastructure. However, the time needed to map the existing geometry makes their use prohibitive. We previously developed class segmentation algorithms to automate digital twinning, however a vast amount of annotated point clouds is needed. Currently, synthetic data generation for automated segmentation is non-existent. We used Helios++ to automatically segment point clouds from 3D models. Our research has the potential to pave the ground for efficient industrial class segmentation.