WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS

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1. Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques Y. Xu & U. Stilla https://doi.org/10.1109/JSTARS.2021.3060568
2. An Automatic Method for Tree Species Point Cloud Segmentation Based on Deep Learning L. Pu et al. https://doi.org/10.1007/s12524-021-01358-x
3. Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds N. Arnold et al. https://doi.org/10.5334/jcaa.66
4. Weld Bead Detection Based on 3D Geometric Features and Machine Learning Approaches P. Rodriguez-Gonzalvez & M. Rodriguez-Martin https://doi.org/10.1109/ACCESS.2019.2891367
5. 基于车载激光点云的路面坑槽检测方法 马. Ma Xinjiang et al. https://doi.org/10.3788/CJL231000
6. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR D. Wang et al. https://doi.org/10.1111/2041-210X.13342
7. E-L 0: Advanced Surface Segmentation of LiDAR Point Clouds in Open-Pit Mine Stepped Terrain T. Chen et al. https://doi.org/10.1109/JSTARS.2025.3592170
8. Hierarchical heterogeneous graph learning for color-missing ALS pointcloud segmentation B. Huang & Y. Zhu https://doi.org/10.1007/s12293-024-00426-2
9. SPPSFormer: High-Quality Superpoint-Based Transformer for Roof Plane Instance Segmentation from Point Clouds C. Zeng et al. https://doi.org/10.3390/rs18132144
10. The 3D narrow butt weld seam detection system based on the binocular consistency correction X. Wang et al. https://doi.org/10.1007/s10845-022-01927-y
11. A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds Z. Yang et al. https://doi.org/10.3390/rs9090936
12. FAIS: Fully Automatic Indoor Surveying Framework of Terrestrial Laser Scanning Point Clouds in Large-Scale Indoor Environments W. Li et al. https://doi.org/10.3390/rs17162863
13. DCARN: Deep Context Aware Recurrent Neural Network for Semantic Segmentation of Large Scale Unstructured 3D Point Cloud S. Mehmood et al. https://doi.org/10.1007/s11063-020-10368-8
14. Local-to-global structure prior guided high-precision point cloud registration framework based on FPP X. Wang et al. https://doi.org/10.1016/j.measurement.2023.112840
15. Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data Z. Yang et al. https://doi.org/10.3390/s18103347
16. Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information? M. Weinmann & M. Weinmann https://doi.org/10.3390/rs10010002
17. Weakly-Supervised Point Cloud Segmentation Combining Pseudo Label Generation and Noise Label Learning A. Deng et al. https://doi.org/10.3724/SP.J.1089.2023.19332
18. Weakly supervised semantic segmentation of airborne laser scanning point clouds Y. Lin et al. https://doi.org/10.1016/j.isprsjprs.2022.03.001
19. Comparison of stand characteristic parameters and biomass estimations from light detection and ranging and structure-from-motion point clouds Z. Xu et al. https://doi.org/10.1117/1.JRS.14.022209
20. Region-Based Edge Convolutions With Geometric Attributes for the Semantic Segmentation of Large-Scale 3-D Point Clouds J. Contreras et al. https://doi.org/10.1109/JSTARS.2020.2998037
21. Road boundary extraction method from mobile laser scanning point clouds G. Xin et al. https://doi.org/10.1088/1361-6501/ad89ec
22. 3D point cloud descriptors: state-of-the-art X. Han et al. https://doi.org/10.1007/s10462-023-10486-4
23. A new weakly supervised approach for ALS point cloud semantic segmentation P. Wang & W. Yao https://doi.org/10.1016/j.isprsjprs.2022.04.016
24. Mesh Oversegmentation with Segmentation-Aware Loss J. Adam et al. https://doi.org/10.1016/j.ins.2022.10.039
25. Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images J. Saura et al. https://doi.org/10.1016/j.heliyon.2019.e01277
26. Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments Z. Zhang et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000959
27. Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds Y. Yao et al. https://doi.org/10.1186/s41074-020-00064-w
28. Region-Enhanced Feature Learning for Scene Semantic Segmentation X. Kang et al. https://doi.org/10.1109/TMM.2023.3342718
29. Integrating OpenStreetMap tags for efficient LiDAR point cloud classification using graph neural networks Y. Chen et al. https://doi.org/10.1080/17538947.2023.2297946
30. Multispectral point cloud superpoint segmentation Q. Wang et al. https://doi.org/10.1007/s11431-023-2528-8
31. Road Segmentation and Environment Labeling for Autonomous Vehicles R. Chen et al. https://doi.org/10.3390/app12147191
32. Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model Y. Xu et al. https://doi.org/10.1109/JSTARS.2019.2951293
33. Dynamic Scene Graph Generation of Point Clouds with Structural Representation Learning C. Qi et al. https://doi.org/10.26599/TST.2023.9010002
34. GECNN for Weakly Supervised Semantic Segmentation of 3D Point Clouds Z. HE et al. https://doi.org/10.1587/transinf.2021EDP7134
35. Conditional Random Field Features and Structure Assessment for Digital Terrain Modeling T. Arevalo-Ramirez & F. Auat Cheein https://doi.org/10.1109/ACCESS.2021.3061371
36. LiDAR remote sensing meets weak supervision: Concepts, methods, and perspectives Y. Gao et al. https://doi.org/10.1016/j.isprsjprs.2026.03.004
37. Supervised Contrastive Learning for Indoor Point Cloud Oversegmentation Y. Sun et al. https://doi.org/10.1109/TMM.2025.3613177
38. A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest X. Shen et al. https://doi.org/10.3390/rs14153842
39. Multigranularity Semantic Labeling of Point Clouds for the Measurement of the Rail Tanker Component With Structure Modeling C. Qi & J. Yin https://doi.org/10.1109/TIM.2020.3027406
40. Segmentation of Individual Trees in TLS Point Clouds via Graph Optimization Y. Liu et al. https://doi.org/10.1109/TGRS.2025.3567357
41. A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds L. Landrieu et al. https://doi.org/10.1016/j.isprsjprs.2017.08.010
42. Using Training Samples Retrieved from a Topographic Map and Unsupervised Segmentation for the Classification of Airborne Laser Scanning Data Z. Yang et al. https://doi.org/10.3390/rs12050877
43. A note on the forward-Douglas–Rachford splitting for monotone inclusion and convex optimization H. Raguet https://doi.org/10.1007/s11590-018-1272-8
44. 3DGraphSeg: A Unified Graph Representation- Based Point Cloud Segmentation Framework for Full-Range High-Speed Railway Environments Y. Geng et al. https://doi.org/10.1109/TII.2023.3246492
45. Point Cloud Semantic Segmentation by Adaptively Fusing Information With Varying Distances Z. Jiang et al. https://doi.org/10.1109/LSP.2024.3416876
46. Road potholes detection from MLS point clouds X. Ma et al. https://doi.org/10.1088/1361-6501/acdb8d
47. GrowSP++: Growing Superpoints and Primitives for Unsupervised 3D Semantic Segmentation Z. Zhang et al. https://doi.org/10.1109/TPAMI.2025.3650165