# Synthetic Point Cloud Generation for Class Segmentation Applications

arXiv: https://arxiv.org/abs/2205.03738 · HTML: https://mlsanity.com/paper/2205.03738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 8 of 8)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://mlsanity.com/paper/1704.01222/) (2017)
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://mlsanity.com/paper/1612.07695/) (2016)
- [3D Shape Segmentation with Projective Convolutional Networks](https://mlsanity.com/paper/1612.02808/) (2016)
- [Dense Human Body Correspondences Using Convolutional Networks](https://mlsanity.com/paper/1511.05904/) (2015)
- [Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning](https://mlsanity.com/paper/2101.09154/) (2021)
- [CLOI: An Automated Benchmark Framework For Generating Geometric Digital Twins Of Industrial Facilities](https://mlsanity.com/paper/2101.01355/) (2021)

## Abstract (arXiv)

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.