Synthetic Point Cloud Generation for Class Segmentation Applications
This addresses the need for annotated data in industrial maintenance automation, but it appears incremental as it builds on existing segmentation algorithms and tools.
The paper tackles the problem of generating synthetic point clouds for class segmentation to automate digital twinning in industrial maintenance, using Helios++ to segment point clouds from 3D models, with the potential to enable efficient segmentation.
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.