SEMANTIC SEGMENTATION OF POINT CLOUDS WITH POINTNET AND KPCONV ARCHITECTURES APPLIED TO RAILWAY TUNNELS

18 citations as recorded by crossref.

1. Fully automated extraction of railtop centerline from mobile laser scanning data A. Kononen et al. 10.1016/j.autcon.2024.105812
2. Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring T. McDonald et al. 10.3390/app122211310
3. Curbside management from MLS and HMLS point clouds to CityGML 3.0 S. González-Collazo et al. 10.1080/10095020.2025.2454522
4. Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks L. Weidner & G. Walton 10.3390/rs16234466
5. Enhancing Railway Detection by Priming Neural Networks with Project Exaptations F. Eickeler & A. Borrmann 10.3390/rs14215482
6. Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations I. Weon et al. 10.3390/app14219685
7. Automated masonry spalling severity segmentation in historic railway tunnels using deep learning and a block face plane fitting approach J. Smith et al. 10.1016/j.tust.2024.106043
8. Automated semantic segmentation of bridge components from large-scale point clouds using a weighted superpoint graph X. Yang et al. 10.1016/j.autcon.2022.104519
9. TripletA-Net: A Deep Learning Model for Automatic Railway Track Extraction from Airborne LiDAR Point Clouds R. Zhang et al. 10.1109/JSTARS.2025.3555292
10. UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels Z. Zhang et al. 10.1016/j.autcon.2022.104456
11. Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN S. Qiu et al. 10.1155/2023/3733799
12. Scanning Technologies to Building Information Modelling: A Review R. Rashdi et al. 10.3390/infrastructures7040049
13. RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation T. Jiang et al. 10.1109/TGRS.2023.3319950
14. FarNet: An Attention-Aggregation Network for Long-Range Rail Track Point Cloud Segmentation Z. Wang et al. 10.1109/TITS.2021.3119900
15. Improved building MEP systems semantic segmentation in point clouds using a novel multi-class dataset and local–global vector transformer network S. Jing et al. 10.1016/j.jobe.2024.110311
16. Self-Attentive Local Aggregation Learning With Prototype Guided Regularization for Point Cloud Semantic Segmentation of High-Speed Railways Z. Wang et al. 10.1109/TITS.2023.3281352
17. Methodology for Activity Unit Segmentation of Design 3D Models Using PointNet Deep Learning Technique J. Lee et al. 10.1007/s12205-023-0816-3
18. WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation B. Qiu et al. 10.1109/TITS.2024.3469546