Automated semantic segmentation of industrial point clouds using ResPointNet++

2021/08/10 by Chao Yin, Boyu Wang, Vincent J.L. Gan +2 · 1 citation
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Infrastructure Maintenance and Monitoring #Remote Sensing and LiDAR Applications

paper · doi:10.1016/j.autcon.2021.103874

openalex publication_date 2021/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Citations

• ShapeNet: An Information-Rich 3D Model Repository
• Joint 2D-3D-Semantic Data for Indoor Scene Understanding
• PointNet: Deep Learning on Point Sets for 3D Classification and\n Segmentation
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
• Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds
• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
• Towards a semantic Construction Digital Twin: Directions for future research
• Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
• Deep Learning for 3D Point Clouds: A Survey
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
• ShapeNet: An Information-Rich 3D Model Repository

Cited by

• Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments