DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

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1. Robust cylinder fitting in laser scanning point cloud data A. Nurunnabi et al. https://doi.org/10.1016/j.measurement.2019.01.095
2. TRACK CENTER ESTIMATION USING TERRESTRIAL LASER SCANNER: FOCUSING ON ARRANGEMENT CHARACTERISTICS OF RAILWAY SPACE K. MATSUSHITA et al. https://doi.org/10.2208/jscejj.24-22012
3. GNSS Precise Point Positioning Enhanced Mobile Laser Scanning Railway Point Cloud Registration G. Xiang et al. https://doi.org/10.1109/JSTARS.2026.3723381
4. Automatic tree detection in varying urban environments using airborne LiDAR data R. dos Santos et al. https://doi.org/10.1007/s12518-026-00720-3
5. ObsGuide: A Plug-and-Play Observability-Guided Residual Selection Method for Accurate LiDAR Odometry Y. Lu et al. https://doi.org/10.1109/JSEN.2026.3704319
6. Road and Building Reconstruction from 3D LiDAR Point Clouds: A Scoping Review N. Krishnakumar et al. https://doi.org/10.1007/s11831-026-10580-0
7. Inferring Radiometric Quality of Multispectral Airborne Laser Scanning Data X. Cheng & W. Yan https://doi.org/10.1007/s41651-025-00213-8
8. TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds C. Chen et al. https://doi.org/10.1016/j.isprsjprs.2025.12.015
9. Self-prompting semantic segmentation of bridge point cloud data using a large computer vision model N. Cui et al. https://doi.org/10.1016/j.autcon.2024.105729
10. Street environment change detection from mobile laser scanning point clouds W. Xiao et al. https://doi.org/10.1016/j.isprsjprs.2015.04.011
11. Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data Z. Kang et al. https://doi.org/10.1109/JSTARS.2018.2869801
12. A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes M. Skrodzki & E. Zimmermann https://doi.org/10.1016/j.cad.2021.103107
13. A branch-trunk-constrained hierarchical clustering method for street trees individual extraction from mobile laser scanning point clouds J. Li et al. https://doi.org/10.1016/j.measurement.2021.110440
14. Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods C. Kim et al. https://doi.org/10.3390/s16020140
15. Supervoxel-based extraction and classification of pole-like objects from MLS point cloud data J. Li & X. Cheng https://doi.org/10.1016/j.optlastec.2021.107562
16. Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas M. Weinmann et al. https://doi.org/10.1016/j.cag.2015.01.006
17. Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting B. Zhao et al. https://doi.org/10.1109/TGRS.2020.2984943
18. Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds N. Arnold et al. https://doi.org/10.5334/jcaa.66
19. Scene graph generation from point cloud panoptic segmentation for indoor scene understanding T. Tiong et al. https://doi.org/10.1007/s11042-026-21856-9
20. Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes M. Mansour et al. https://doi.org/10.3390/infrastructures9050083
21. Multi-KPConv: deep learning-based LiDAR point cloud ground point extraction for complex terrains on the Loess Plateau C. Zhu et al. https://doi.org/10.1080/17538947.2025.2556235
22. 3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net J. Li et al. https://doi.org/10.1109/ACCESS.2020.3044166
23. Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds T. Teo & S. Huang https://doi.org/10.3390/rs61212686
24. Automated Schedule and Cost Control Using 3D Sensing Technologies A. ElQasaby et al. https://doi.org/10.3390/app13020783
25. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers M. Weinmann et al. https://doi.org/10.1016/j.isprsjprs.2015.01.016
26. Extraction of Liana Stems Using Geometric Features from Terrestrial Laser Scanning Point Clouds T. Han & G. Sánchez-Azofeifa https://doi.org/10.3390/rs14164039
27. Automated Extraction of Rail Point Clouds by Multi-Scale Dimensional Features From MLS Data F. Han et al. https://doi.org/10.1109/ACCESS.2023.3262732
28. ITU-Campus3D: A Campus-Scale Urban Mobile Mapping Benchmark for Semantic Segmentation K. Aksu et al. https://doi.org/10.1109/ACCESS.2026.3682205
29. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. https://doi.org/10.3390/s19081852
30. A novel approach to de-noising 3D point clouds using mean-shift based clustering algorithm s. kamalou et al. https://doi.org/10.52547/jgit.9.4.1
31. Passability-Based Local Planner Using Growing Neural Gas for an Autonomous Mobile Robot K. Ozasa et al. https://doi.org/10.1109/ACCESS.2024.3499364
32. NDT-LOAM: A Real-Time Lidar Odometry and Mapping With Weighted NDT and LFA S. Chen et al. https://doi.org/10.1109/JSEN.2021.3135055
33. Refraction error correction of Airborne LiDAR Bathymetry data considering sea surface waves W. Xu et al. https://doi.org/10.1016/j.jag.2021.102402
34. Evaluation of data subsampling and neighbourhood selection for mobile LiDAR data classification M. Mohamed et al. https://doi.org/10.1016/j.ejrs.2021.04.003
35. Label-Efficient Point Cloud Semantic Segmentation: A Holistic Active Learning Approach X. Shi et al. https://doi.org/10.1142/S281103232440006X
36. 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
37. [Paper] Automatic Detection of Sharp Edges from Point Cloud Using Surface Universality Rating S. Erdenebayar et al. https://doi.org/10.3169/mta.12.9
38. Georeferenced Point Clouds: A Survey of Features and Point Cloud Management J. Otepka et al. https://doi.org/10.3390/ijgi2041038
39. Individual Extraction of Street Trees From MLS Point Clouds Based on Tree Nonphotosynthetic Components Clustering J. Li et al. https://doi.org/10.1109/JSTARS.2023.3281787
40. Resilient odometry via hierarchical adaptation S. Zhao et al. https://doi.org/10.1126/scirobotics.adv1818
41. Street Tree Crown Detection with Mobile Laser Scanning Data Using a Grid Index and Local Features Q. Li et al. https://doi.org/10.1007/s41064-022-00208-w
42. A Multiview Stereo Algorithm Based on Image Segmentation Guided Generation of Planar Prior for Textureless Regions of Artificial Scenes N. Huang et al. https://doi.org/10.1109/JSTARS.2023.3237588
43. An adaptive stair-ascending gait generation approach based on depth camera for lower limb exoskeleton X. Zhao et al. https://doi.org/10.1063/1.5109741
44. SAGNet: Enhancing Sparse Power Line Point Cloud Segmentation With EdgeConv and Structure Tensor Gating Y. He et al. https://doi.org/10.1109/ACCESS.2026.3674351
45. Removal of LiDAR Negative Outliers Based on Retroreflective Surface H. Gao et al. https://doi.org/10.1109/JSTARS.2025.3585534
46. Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach R. dos Santos & M. Galo https://doi.org/10.1109/JSTARS.2024.3391050
47. A Real-Time SCFNR-Based Point Cloud Denoising Method for Autonomous Driving in Adverse Mining Environments R. Liu et al. https://doi.org/10.1109/TIM.2026.3706226
48. Improving Vegetation Mapping from LiDAR Point Clouds Using a Transmissivity-Based Feature M. Hess et al. https://doi.org/10.3390/rs18152602
49. Extracting Structural Elements From 3D Point Clouds in Indoor Environments via Machine Learning Techniques K. Aksu & H. Demirel https://doi.org/10.1109/ACCESS.2024.3423425
50. Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds X. Ning et al. https://doi.org/10.1007/s11042-021-11328-7
51. A Knowledge-Based Multi-Scale Adaptive Classification Approach for Mobile Laser Scanning Point Clouds in Urban Scenes M. Zheng et al. https://doi.org/10.1109/ACCESS.2023.3335607
52. 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
53. 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
54. Monitoring of Water and Tillage Soil Erosion in Agricultural Basins, a Comparison of Measurements Acquired by Differential Interferometric Analysis of Sentinel TopSAR Images and a Terrestrial LIDAR System F. Sánchez-Crespo et al. https://doi.org/10.3390/land12020408
55. Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques A. Capolupo https://doi.org/10.3390/drones5040145
56. TUC-Net: A Point Cloud Segmentation Network Based on Neighborhood Feature Perception Aggregation for Tunnels Under Construction X. Zhang et al. https://doi.org/10.1109/TITS.2025.3542500
57. Modeling 3D Canopy Structure and Transmitted PAR Using Terrestrial LiDAR R. Cifuentes et al. https://doi.org/10.1080/07038992.2017.1286937
58. Automatic Segmentation and Shape-Based Classification of Retro-Reflective Traffic Signs from Mobile LiDAR Data B. Riveiro et al. https://doi.org/10.1109/JSTARS.2015.2461680
59. INF-PCA: Implicit Neural Field-Based Interactive Point Cloud Semantic Annotation C. Liu et al. https://doi.org/10.1109/TITS.2024.3496938
60. Linking persistent scatterers to airborne laser scanning points for identifying real objects reflecting SAR signal N. Wielgocka et al. https://doi.org/10.1515/jag-2024-0084
61. IGICP: Intensity and Geometry Enhanced LiDAR Odometry L. He et al. https://doi.org/10.1109/TIV.2023.3336376
62. Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds S. Singh & J. Sreevalsan-Nair https://doi.org/10.1109/LGRS.2021.3099935
63. 3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data M. Letard et al. https://doi.org/10.1016/j.isprsjprs.2023.11.022
64. DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization W. Wu et al. https://doi.org/10.1016/j.isprsjprs.2025.01.036
65. Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN J. Zhang et al. https://doi.org/10.1080/10106049.2022.2034988
66. Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and Aggregation A. Umam et al. https://doi.org/10.1109/TMM.2024.3371294
67. UAV building point cloud contour extraction based on the feature recognition of adjacent points distribution C. Wu et al. https://doi.org/10.1016/j.measurement.2024.114519
68. Topologically Aware Building Rooftop Reconstruction From Airborne Laser Scanning Point Clouds D. Chen et al. https://doi.org/10.1109/TGRS.2017.2738439
69. A spatial–spectral classification framework for multispectral LiDAR S. Shi et al. https://doi.org/10.1080/10095020.2023.2208611
70. 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
71. Automatic Registration of Terrestrial and Airborne Point Clouds Using Building Outline Features X. Cheng et al. https://doi.org/10.1109/JSTARS.2017.2788054
72. Handcrafted point-voxel feature strategies for semantic classification of urban mobile laser scanning data S. Seyfeli & A. Ok https://doi.org/10.1080/22797254.2026.2655370
73. A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation Y. Li et al. https://doi.org/10.3390/rs9040331
74. Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data A. Dittrich et al. https://doi.org/10.1016/j.isprsjprs.2017.02.012
75. 3D Feature Engineering for Deep Semantic Segmentation of Indoor Point Clouds M. Mérizette et al. https://doi.org/10.1111/phor.70047
76. Geometric features extraction and automated classification for damage assessment using LiDAR data M. Apoorva & N. Vaani https://doi.org/10.1007/s13349-025-01036-3
77. Classification of Terrestrial Laser Scanning Data With Density-Adaptive Geometric Features M. Chen et al. https://doi.org/10.1109/LGRS.2018.2860589
78. Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets A. Sánchez-Rodríguez et al. https://doi.org/10.1016/j.autcon.2018.09.014
79. Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns W. Hao & M. Ran https://doi.org/10.1080/01431161.2024.2394235
80. Evaluation of surface damage for in-service deteriorated agricultural concrete headworks using 3D point clouds by laser scanning method K. Shibano et al. https://doi.org/10.1007/s10333-023-00965-3
81. Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors I. Pârvu et al. https://doi.org/10.2478/jaes-2020-0023
82. Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification P. Hsu & Z. Zhuang https://doi.org/10.3390/rs12223713
83. Integrating topographic knowledge into point cloud simplification for terrain modelling J. Chen et al. https://doi.org/10.1080/13658816.2023.2180801
84. Railway Overhead Contact System Point Cloud Classification X. Chen et al. https://doi.org/10.3390/s21154961
85. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning A. Serna & B. Marcotegui https://doi.org/10.1016/j.isprsjprs.2014.03.015
86. Integrating topographic features and patch matching into point cloud restoration for terrain modelling J. Chen et al. https://doi.org/10.1080/17538947.2023.2277797
87. 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
88. Semantic Segmentation and 3D Reconstruction of Steel Girder Bridge from TLS Point Cloud Integrating Domain Knowledge T. Mizoguchi https://doi.org/10.20965/ijat.2026.p0254
89. Street-side vehicle detection, classification and change detection using mobile laser scanning data W. Xiao et al. https://doi.org/10.1016/j.isprsjprs.2016.02.007
90. Automatic cables segmentation from a substation device based on 3D point cloud Q. Yuan et al. https://doi.org/10.1007/s00138-022-01358-y
91. 3D Point Cloud Semantic Segmentation Through Functional Data Analysis M. Oviedo de la Fuente et al. https://doi.org/10.1007/s13253-023-00567-w
92. Street tree segmentation from mobile laser scanning data Q. Li et al. https://doi.org/10.1080/01431161.2020.1754495
93. A review and future directions of techniques for extracting powerlines and pylons from LiDAR point clouds Y. Shen et al. https://doi.org/10.1016/j.jag.2024.104056
94. Adaptive neighbourhood recovery method for machine learning based 3D point cloud classification J. Xue et al. https://doi.org/10.1080/01431161.2022.2162354
95. Automatic Stem Detection in Terrestrial Laser Scanning Data With Distance-Adaptive Search Radius M. Chen et al. https://doi.org/10.1109/TGRS.2017.2787782
96. 3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning J. Pan et al. https://doi.org/10.1016/j.isprsjprs.2021.11.007
97. Health monitoring of ground support system through point-cloud processing: rockbolts extraction phase A. Patwardhan & R. Karim https://doi.org/10.1007/s13198-025-02758-9