Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features

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1. Street environment change detection from mobile laser scanning point clouds W. Xiao et al. https://doi.org/10.1016/j.isprsjprs.2015.04.011
2. Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis R. Rashdi et al. https://doi.org/10.1080/22797254.2024.2316304
3. Application of Stochastic Point-Based Rendering to Laser-Scanned Point Clouds of Various Cultural Heritage Objects K. Hasegawa et al. https://doi.org/10.20965/ijat.2018.p0348
4. 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
5. Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method S. Yang et al. https://doi.org/10.1080/17538947.2024.2385081
6. Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-Supervised Learning J. Mei et al. https://doi.org/10.1109/TITS.2019.2919741
7. Comparison of Different Feature Sets for TLS Point Cloud Classification Q. Li & X. Cheng https://doi.org/10.3390/s18124206
8. Edge highlighting of laser-scanned point clouds improves the accuracy of perceived depth in transparent multi-view 3D visualizations D. Aoi et al. https://doi.org/10.1142/S1793962324500211
9. Local and global encoder network for semantic segmentation of Airborne laser scanning point clouds Y. Lin et al. https://doi.org/10.1016/j.isprsjprs.2021.04.016
10. Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures M. Mohammadi et al. https://doi.org/10.3390/ijgi8120527
11. An Over-Segmentation-Based Uphill Clustering Method for Individual Trees Extraction in Urban Street Areas From MLS Data J. Li et al. https://doi.org/10.1109/JSTARS.2021.3051653
12. Learning multiscale spatial context for three-dimensional point cloud semantic segmentation Y. Wang & S. Xiao https://doi.org/10.1117/1.JEI.29.6.063005
13. 3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning L. Wang et al. https://doi.org/10.1109/ACCESS.2019.2909742
14. Active and incremental learning for semantic ALS point cloud segmentation Y. Lin et al. https://doi.org/10.1016/j.isprsjprs.2020.09.003
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. Assessing the impact of 3D point neighborhood size selection on unsupervised spall classification with 3D bridge point clouds V. Kasireddy & B. Akinci https://doi.org/10.1016/j.aei.2022.101624
17. Outdoor Scene Understanding Based on Multi-Scale PBA Image Features and Point Cloud Features Y. Liu et al. https://doi.org/10.3390/s19204546
18. Evaluation of LiDAR-Derived Features Relevance and Training Data Minimization for 3D Point Cloud Classification S. Morsy & A. Shaker https://doi.org/10.3390/rs14235934
19. 3D multi-robot patrolling with a two-level coordination strategy L. Freda et al. https://doi.org/10.1007/s10514-018-09822-3
20. A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method P. Pahlavani et al. https://doi.org/10.1080/01431161.2015.1066524
21. Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing K. Liu et al. https://doi.org/10.3390/rs12172849
22. 3D Lidar Point Cloud Segmentation for Automated Driving R. Abbasi et al. https://doi.org/10.1109/MITS.2023.3325854
23. Geometric characterization and segmentation of historic buildings using classification algorithms and convolutional networks in HBIM J. Moyano et al. https://doi.org/10.1016/j.autcon.2024.105728
24. A Maximum Entropy-Based Optimal Neighbor Selection for Multispectral Airborne LiDAR Point Cloud Classification G. Jiang et al. https://doi.org/10.1109/TGRS.2023.3323963
25. A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration K. Dröder et al. https://doi.org/10.1016/j.procir.2018.02.010
26. Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation G. Kermarrec et al. https://doi.org/10.3390/rs14205099
27. 3-D LiDAR-Based Place Recognition Techniques: A Review of the Past Ten Years Z. Du et al. https://doi.org/10.1109/TIM.2024.3403194
28. Power Line Extraction and Reconstruction Methods from Laser Scanning Data: A Literature Review N. Munir et al. https://doi.org/10.3390/rs15040973
29. Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes M. Mansour et al. https://doi.org/10.3390/infrastructures9050083
30. 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
31. Building Extraction from Terrestrial Laser Scanning Data with Density of Projected Points on Polar Grid and Adaptive Threshold M. Chen et al. https://doi.org/10.3390/rs13214392
32. EIDU-Net: edge-preserved inception DenseGCN U-Net for LiDAR point cloud segmentation X. Xu et al. https://doi.org/10.1038/s41598-024-74690-0
33. RLS-LCD: An Efficient Loop Closure Detection for Rotary-LiDAR Scans Q. Zhang et al. https://doi.org/10.1109/JSEN.2023.3345649
34. Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data E. Dey et al. https://doi.org/10.3390/rs13081520
35. Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales J. Zhou et al. https://doi.org/10.3390/s19081852
36. A hierarchical closed-loop constrained registration framework for ALB multi-strip registration D. Pu et al. https://doi.org/10.1088/1361-6501/ae5cbf
37. Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment M. Areti & Z. Hasnain https://doi.org/10.1061/(ASCE)CP.1943-5487.0001036
38. Geometric Feedback System for Robotic Spraying V. Frangez et al. https://doi.org/10.1007/s41064-022-00219-7
39. Hierarchical strategy and correspondence optimization for ALB point cloud registration D. Pu et al. https://doi.org/10.1088/1361-6501/ae2650
40. Towards a Meaningful 3D Map Using a 3D Lidar and a Camera J. Jeong et al. https://doi.org/10.3390/s18082571
41. Multi-Scale Local Context Embedding for LiDAR Point Cloud Classification R. Huang et al. https://doi.org/10.1109/LGRS.2019.2927779
42. 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
43. Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization W. Li et al. https://doi.org/10.3390/rs13132526
44. Deep Learning-based Highlighting Visualization for Soft Edges in Large-Scale Scanned Point Clouds W. Li et al. https://doi.org/10.15748/jasse.9.278
45. Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection X. Liu et al. https://doi.org/10.3390/s19245419
46. An Adaptive End-to-End Classification Approach for Mobile Laser Scanning Point Clouds Based on Knowledge in Urban Scenes M. Zheng et al. https://doi.org/10.3390/rs11020186
47. Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds V. Kasireddy & B. Akinci https://doi.org/10.1061/JCCEE5.CPENG-5041
48. Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds A. Al-Rawabdeh et al. https://doi.org/10.3390/rs12071224
49. Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points M. Bruggisser et al. https://doi.org/10.3390/rs11020189
50. Validation of fracture data recognition in rock masses by automated plane detection in 3D point clouds T. Drews et al. https://doi.org/10.1016/j.ijrmms.2018.06.023
51. Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data A. Nurunnabi et al. https://doi.org/10.1109/TGRS.2016.2551546
52. Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization Z. Cai et al. https://doi.org/10.1080/15481603.2019.1695406
53. A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment B. Xiang et al. https://doi.org/10.1109/TGRS.2019.2916625
54. Path Segmentation from Point Cloud Data for Autonomous Navigation K. Rajathi et al. https://doi.org/10.3390/app13063977
55. A multi-scale fully convolutional network for semantic labeling of 3D point clouds M. Yousefhussien et al. https://doi.org/10.1016/j.isprsjprs.2018.03.018
56. LPD-AE: Latent Space Representation of Large-Scale 3D Point Cloud C. Suo et al. https://doi.org/10.1109/ACCESS.2020.2999727
57. Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds T. Tran et al. https://doi.org/10.3390/s18020448
58. Segmentation of Façades from Urban 3D Point Clouds Using Geometrical and Morphological Attribute-Based Operators A. Serna et al. https://doi.org/10.3390/ijgi5010006
59. RING++: Roto-Translation Invariant Gram for Global Localization on a Sparse Scan Map X. Xu et al. https://doi.org/10.1109/TRO.2023.3303035
60. SegMap: Segment-based mapping and localization using data-driven descriptors R. Dubé et al. https://doi.org/10.1177/0278364919863090
61. Correspondence-driven plane-based M3C2 for lower uncertainty in 3D topographic change quantification V. Zahs et al. https://doi.org/10.1016/j.isprsjprs.2021.11.018
62. Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation W. Gruszczyński et al. https://doi.org/10.1016/j.isprsjprs.2017.02.015
63. Segmentation-based classification for 3D point clouds in the road environment B. Xiang et al. https://doi.org/10.1080/01431161.2018.1455235
64. Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds P. Wernette https://doi.org/10.3390/rs16122169
65. Quantifying Multi-Scale Performance of Geometric Features for Efficient Extraction of Insulators from Point Clouds J. Tang et al. https://doi.org/10.3390/rs15133339
66. High-Visibility Edge-Highlighting Visualization of 3D Scanned Point Clouds Based on Dual 3D Edge Extraction Y. Yamada et al. https://doi.org/10.3390/rs16193750
67. Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR M. Herrero-Huerta et al. https://doi.org/10.3389/fpls.2018.00189
68. Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest N. Brodić et al. https://doi.org/10.3390/rs14215345
69. Urban 3D modeling using mobile laser scanning: a review C. Wang et al. https://doi.org/10.1016/j.vrih.2020.05.003
70. 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
71. 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
72. Multimetric Performance Evaluation of Smartphone-Based Close-Range Photogrammetry on a Reinforced Concrete Structure A. Devkota & M. Gheisari https://doi.org/10.1061/JCCEE5.CPENG-7397
73. 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
74. 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
75. Context-Aware Network for Semantic Segmentation Toward Large-Scale Point Clouds in Urban Environments C. Liu et al. https://doi.org/10.1109/TGRS.2022.3182776
76. G3Reg: Pyramid Graph-Based Global Registration Using Gaussian Ellipsoid Model Z. Qiao et al. https://doi.org/10.1109/TASE.2024.3394519
77. Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications Q. Wang et al. https://doi.org/10.1007/s11831-019-09320-4
78. Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier M. Mohamed et al. https://doi.org/10.1080/10106049.2022.2102218
79. Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique D. Chakraborty et al. https://doi.org/10.1371/journal.pone.0307138
80. Incorporating Human Domain Knowledge in 3-D LiDAR-Based Semantic Segmentation J. Mei & H. Zhao https://doi.org/10.1109/TIV.2019.2955851
81. Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection S. Shi et al. https://doi.org/10.3390/rs13204118
82. Data enrichment for semantic segmentation of point clouds for the generation of geometric-semantic road models D. Crampen & J. Blankenbach https://doi.org/10.3389/fbuil.2025.1607375
83. Multi-Source Data Fusion: Comparison and Application of Measurement Technologies in the Renovation of Old Residential Communities 可. 杜 https://doi.org/10.12677/gst.2026.142013
84. Incremental-Segment-Based Localization in 3-D Point Clouds R. Dube et al. https://doi.org/10.1109/LRA.2018.2803213
85. Semantic Segmentation for Digital Archives of Borobudur Reliefs Based on Soft-Edge Enhanced Deep Learning S. Ji et al. https://doi.org/10.3390/rs15040956
86. BICANet: LiDAR Point Cloud Classification Network Based on Coordinate Attention and Blueprint Separation Involution Neural Network G. Zhang et al. https://doi.org/10.1109/JSEN.2023.3323047
87. Genetic Algorithm Empowering Unsupervised Learning for Optimizing Building Segmentation from Light Detection and Ranging Point Clouds M. Sulaiman et al. https://doi.org/10.3390/rs16193603
88. Hierarchical Joint Optimization Algorithm With Joint Weight Guidance for ALB Multistrip Registration D. Pu et al. https://doi.org/10.1109/JSTARS.2026.3671374
89. A spatial–spectral classification framework for multispectral LiDAR S. Shi et al. https://doi.org/10.1080/10095020.2023.2208611
90. 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
91. Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds I. Toschi et al. https://doi.org/10.1016/j.isprsjprs.2020.12.005
92. A Method for Detecting Dynamic Objects Using 2D LiDAR Based on Scan Matching M. Mihálik et al. https://doi.org/10.3390/app12115641
93. Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet E. Barnefske & H. Sternberg https://doi.org/10.1109/ACCESS.2022.3233411
94. Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests H. Ni et al. https://doi.org/10.3390/rs9030288
95. 3D colored point cloud classification of a deep-sea cold-water coral and sponge habitat using geometric features and machine learning algorithms S. Morsy et al. https://doi.org/10.3389/frsen.2025.1680353
96. Revue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile S. Daniel https://doi.org/10.1139/geomatica-2018-0001
97. Point Cloud Data Processing Optimization in Spectral and Spatial Dimensions Based on Multispectral Lidar for Urban Single-Wood Extraction S. Shi et al. https://doi.org/10.3390/ijgi12030090
98. Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean M. Herrero-Huerta et al. https://doi.org/10.1186/s13007-020-00620-6
99. Toward transparent intelligence: Explainable stacked ensembles learning for LiDAR point cloud segmentation A. Bondhon et al. https://doi.org/10.1371/journal.pone.0345125
100. Edge highlighting with depth-dependent opacity gradation of laser-scanned point clouds improves the accuracy of perceived depth in transparent multi-view 3D visualizations D. Aoi et al. https://doi.org/10.1007/s12650-024-01014-9
101. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest C. Alvites et al. https://doi.org/10.3390/rs13214265
102. Utilizing UAV and 3D Computer Vision for Visual Inspection of a Large Gravity Dam A. Khaloo et al. https://doi.org/10.3389/fbuil.2018.00031
103. Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging D. Wang et al. https://doi.org/10.3390/f9050252
104. A Novel Pallet Detection Method for Automated Guided Vehicles Based on Point Cloud Data Y. Shao et al. https://doi.org/10.3390/s22208019
105. A Small-Object-Detection Algorithm Based on LiDAR Point-Cloud Clustering for Autonomous Vehicles Z. Duan et al. https://doi.org/10.3390/s24165423
106. Total leaf area estimation based on point cloud reduction and resolution grid construction using mobile multi-layer LiDAR scanning Q. Li & L. Ding https://doi.org/10.1016/j.optlastec.2024.112394