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

11 citations as recorded by crossref.

1. Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring T. McDonald et al. 10.3390/app122211310
2. 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
3. Enhancing Railway Detection by Priming Neural Networks with Project Exaptations F. Eickeler & A. Borrmann 10.3390/rs14215482
4. Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN S. Qiu et al. 10.1155/2023/3733799
5. Scanning Technologies to Building Information Modelling: A Review R. Rashdi et al. 10.3390/infrastructures7040049
6. RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation T. Jiang et al. 10.1109/TGRS.2023.3319950
7. FarNet: An Attention-Aggregation Network for Long-Range Rail Track Point Cloud Segmentation Z. Wang et al. 10.1109/TITS.2021.3119900
8. 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
9. 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
10. Methodology for Activity Unit Segmentation of Design 3D Models Using PointNet Deep Learning Technique J. Lee et al. 10.1007/s12205-023-0816-3
11. Automatic Point Cloud Semantic Segmentation of Complex Railway Environments D. Lamas et al. 10.3390/rs13122332