SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

393 citations as recorded by crossref.

1. DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds L. Chen et al. https://doi.org/10.1016/j.jag.2021.102650
2. 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
3. Extraction of local structure information of point clouds through space-filling curve for semantic segmentation X. Xiang et al. https://doi.org/10.1016/j.jag.2022.103027
4. SFL-NET: Slight Filter Learning Network for Point Cloud Semantic Segmentation X. Li et al. https://doi.org/10.1109/TGRS.2023.3313876
5. Point Cloud Convolution Network Based on Spatial Location Correspondence J. Xv et al. https://doi.org/10.3390/ijgi11120591
6. 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
7. 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
8. Assessing the visibility of urban greenery using MLS LiDAR data L. Tang et al. https://doi.org/10.1016/j.landurbplan.2022.104662
9. Deep-Learning-Based 3-D Surface Reconstruction—A Survey A. Farshian et al. https://doi.org/10.1109/JPROC.2023.3321433
10. Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds X. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2024.11.012
11. See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning Z. Xi et al. https://doi.org/10.1016/j.isprsjprs.2020.08.001
12. Exploring Semantic Information Extraction From Different Data Forms in 3D Point Cloud Semantic Segmentation A. Zhang et al. https://doi.org/10.1109/ACCESS.2023.3287940
13. Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review D. Guastella & G. Muscato https://doi.org/10.3390/s21010073
14. Intelligent 3D Objects Classification for Vehicular Ad Hoc Network Based on Lidar and Deep Learning Approaches P. de Sousa et al. https://doi.org/10.1109/TITS.2021.3119132
15. CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets E. Agapaki & I. Brilakis https://doi.org/10.1016/j.aei.2020.101121
16. Semantic Segmentation and Reconstruction of Indoor Scene Point Clouds W. HAO et al. https://doi.org/10.4316/AECE.2024.03001
17. A new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentation A. SAĞLAM & N. AKHAN BAYKAN https://doi.org/10.26833/ijeg.709212
18. A multi-point focus transformer approach for large-scale ALS point cloud ground filtering T. Liu et al. https://doi.org/10.1080/01431161.2024.2443604
19. 3D point cloud semantic segmentation toward large-scale unstructured agricultural scene classification Y. Chen et al. https://doi.org/10.1016/j.compag.2021.106445
20. Learning-assisted scan-vs-BIM registration and quantification for robust progress monitoring J. Fernandes et al. https://doi.org/10.1080/15623599.2026.2635999
21. Multi-Feature Aggregation for Semantic Segmentation of an Urban Scene Point Cloud J. Chen et al. https://doi.org/10.3390/rs14205134
22. A Review of Deep Learning-Based Semantic Segmentation for Point Cloud J. Zhang et al. https://doi.org/10.1109/ACCESS.2019.2958671
23. Consistent Semantic Annotation of Outdoor Datasets via 2D/3D Label Transfer R. Tylecek & R. Fisher https://doi.org/10.3390/s18072249
24. Incorporating Human Domain Knowledge in 3-D LiDAR-Based Semantic Segmentation J. Mei & H. Zhao https://doi.org/10.1109/TIV.2019.2955851
25. Rotation invariance and equivariance in 3D deep learning: a survey J. Fei & Z. Deng https://doi.org/10.1007/s10462-024-10741-2
26. Deep learning on multi-view sequential data: a survey Z. Xie et al. https://doi.org/10.1007/s10462-022-10332-z
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. Revisiting the Effectiveness of 3D Object Recognition Benchmarks H. Song & S. Lee https://doi.org/10.1109/ACCESS.2023.3322433
29. A Survey on Non-Photorealistic Rendering Approaches for Point cloud Visualization O. Wegen et al. https://doi.org/10.1109/TVCG.2024.3402610
30. Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation From MLS A. Aditya et al. https://doi.org/10.1109/TGRS.2024.3381976
31. A Laplacian for Nonmanifold Triangle Meshes N. Sharp & K. Crane https://doi.org/10.1111/cgf.14069
32. High-Precision Edge Geometric Measurement of Thin-Walled Structures Using 3-D LiDAR via Spherical Harmonic Feature Enhancement Y. Leng et al. https://doi.org/10.1109/JSEN.2026.3709313
33. Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences A. Afifi et al. https://doi.org/10.1109/TGRS.2023.3340293
34. SSGAM-Net: A Hybrid Semi-Supervised and Supervised Network for Robust Semantic Segmentation Based on Drone LiDAR Data H. Wu et al. https://doi.org/10.3390/rs16010092
35. Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes J. Han et al. https://doi.org/10.1109/TIP.2021.3106811
36. Canopy3D-Net: Semantic segmentation of fruit tree canopies based on 3D point clouds Z. Sun et al. https://doi.org/10.1016/j.atech.2025.101673
37. 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
38. A Survey of Point Cloud Completion Z. Zhuang et al. https://doi.org/10.1109/JSTARS.2024.3362476
39. GA-NET: Global Attention Network for Point Cloud Semantic Segmentation S. Deng & Q. Dong https://doi.org/10.1109/LSP.2021.3082851
40. Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış M. DEMİRTAŞ https://doi.org/10.24012/dumf.1067736
41. A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification Z. Wang et al. https://doi.org/10.1109/TGRS.2018.2829625
42. Towards accurate urban scene understanding using point clouds: The SemanticUrban dataset Y. Fang et al. https://doi.org/10.1016/j.eswa.2026.132949
43. Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization W. Li et al. https://doi.org/10.1016/j.isprsjprs.2020.01.021
44. Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments F. Mohammadi Amin et al. https://doi.org/10.1007/s10846-025-02290-9
45. A shimming method based on outer mold line control of thin-walled parts S. Li et al. https://doi.org/10.1177/09544054221118795
46. Constructing and Maintaining Road Geometric Digital Twins D. Davletshina et al. https://doi.org/10.1061/JCCEE5.CPENG-6239
47. Lidar Point Semantic Segmentation Using Dual Attention Mechanism H. Wang et al. https://doi.org/10.1007/s10946-023-10127-9
48. Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds S. González-Collazo et al. https://doi.org/10.1016/j.eswa.2023.121842
49. Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage W. Li et al. https://doi.org/10.3390/rs16152758
50. 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
51. Learning-Based Reflection-Aware Virtual Point Removal for Large-Scale 3D Point Clouds O. Lee et al. https://doi.org/10.1109/LRA.2023.3329365
52. PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest Environments J. Li et al. https://doi.org/10.3390/f14122276
53. Classification and Segmentation of Mining Area Objects in Large-Scale Spares Lidar Point Cloud Using a Novel Rotated Density Network Y. Yan et al. https://doi.org/10.3390/ijgi9030182
54. PECo: A Point-Edge Collaborative Framework for Global-Aware Urban Building Contouring From Unstructured Point Clouds S. Di et al. https://doi.org/10.1109/TGRS.2023.3342807
55. Multiscale voxel feature fusion network for large scale noisy point cloud completion in cultural heritage restoration W. Li et al. https://doi.org/10.1038/s40494-026-02331-y
56. AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-Time High-Fidelity LiDAR Simulation J. Richa et al. https://doi.org/10.3390/rs14246262
57. The role of machine intelligence in photogrammetric 3D modeling – an overview and perspectives R. Qin & A. Gruen https://doi.org/10.1080/17538947.2020.1805037
58. Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry A. Kamran-Pishhesari et al. https://doi.org/10.3390/technologies12030040
59. PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry R. Zhang et al. https://doi.org/10.1021/acs.jctc.3c01420
60. DenseKPNET: Dense Kernel Point Convolutional Neural Networks for Point Cloud Semantic Segmentation Y. Li et al. https://doi.org/10.1109/TGRS.2022.3162582
61. Active Learning Based 3D Semantic Labeling From Images and Videos M. Rong et al. https://doi.org/10.1109/TCSVT.2021.3079991
62. Cascaded Contextual Reasoning for Large-Scale Point Cloud Semantic Segmentation F. Zhang & X. Xia https://doi.org/10.1109/ACCESS.2023.3248963
63. Enhanced Local Feature Learning With Simple Offset Attention for Semantic Segmentation of Large-Scale Point Clouds D. Chen et al. https://doi.org/10.1109/TGRS.2024.3453966
64. Image-based laser point cloud building facade structure extraction method by considering semantic information X. Hu et al. https://doi.org/10.1038/s40494-025-01728-5
65. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network H. Luo et al. https://doi.org/10.1109/LGRS.2022.3208589
66. Collapsed Building Detection Using 3D Point Clouds and Deep Learning H. Xiu et al. https://doi.org/10.3390/rs12244057
67. A new point cloud simplification method with feature and integrity preservation by partition strategy S. Wang et al. https://doi.org/10.1016/j.measurement.2022.111173
68. Object-based building instance segmentation from airborne LiDAR point clouds W. Yang et al. https://doi.org/10.1080/01431161.2022.2145582
69. A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning Z. Ballouch et al. https://doi.org/10.3390/rs14143415
70. Cross modal networks for point cloud semantic segmentation of Chinese ancient buildings Z. Xie et al. https://doi.org/10.1038/s40494-025-01701-2
71. Local Transformer Network on 3D Point Cloud Semantic Segmentation Z. Wang et al. https://doi.org/10.3390/info13040198
72. Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling Q. Hu et al. https://doi.org/10.1109/TPAMI.2021.3083288
73. 3-D Line Matching Network Based on Matching Existence Guidance and Knowledge Distillation J. Tang et al. https://doi.org/10.1109/JIOT.2024.3429352
74. Inferring Point Cloud Quality via Graph Similarity Q. Yang et al. https://doi.org/10.1109/TPAMI.2020.3047083
75. AI-Powered LiDAR Point Cloud Understanding and Processing: An Updated Survey S. Jia et al. https://doi.org/10.1109/TITS.2025.3568500
76. A Graph Aggregation Convolution and Attention Mechanism Based Semantic Segmentation Method for Sparse Lidar Point Cloud Data T. Zheng et al. https://doi.org/10.1109/ACCESS.2023.3339657
77. Continuous Mapping Convolution for Large-Scale Point Clouds Semantic Segmentation K. Yan et al. https://doi.org/10.1109/LGRS.2021.3107006
78. The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review J. Zhao et al. https://doi.org/10.3390/rs13204029
79. Robust Geometry–Hue Point Cloud Registration via Hybrid Adaptive Residual Optimization Y. Xie et al. https://doi.org/10.3390/ijgi15010022
80. Curbside management from MLS and HMLS point clouds to CityGML 3.0 S. González-Collazo et al. https://doi.org/10.1080/10095020.2025.2454522
81. Weakly supervised point cloud semantic segmentation based on scene consistency Y. Niu et al. https://doi.org/10.1007/s10489-024-05822-2
82. Boundary-Aware and Semiautomatic Segmentation of 3-D Object in Point Clouds H. Luo et al. https://doi.org/10.1109/LGRS.2020.2987747
83. RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration Y. Wang et al. https://doi.org/10.3390/rs17020345
84. PSNet: Patch-Based Self-Attention Network for 3D Point Cloud Semantic Segmentation H. Yi et al. https://doi.org/10.3390/rs17122012
85. Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game D. Zai et al. https://doi.org/10.1016/j.isprsjprs.2017.10.001
86. Semantic-guided 3D building reconstruction from triangle meshes S. Wang et al. https://doi.org/10.1016/j.jag.2023.103324
87. Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global R. Huang et al. https://doi.org/10.1016/j.isprsjprs.2020.02.020
88. GLSNet++: Global and Local-Stream Feature Fusion for LiDAR Point Cloud Semantic Segmentation Using GNN Demixing Block R. Bao et al. https://doi.org/10.1109/JSEN.2023.3345747
89. Advancing ALS Applications with Large-Scale Pre-Training: Framework, Dataset, and Downstream Assessment H. Xiu et al. https://doi.org/10.3390/rs17111859
90. Learning and fusing the rasterized and serialized point cloud for 3D semantic segmentation in railway scenes N. Sun et al. https://doi.org/10.1016/j.eswa.2025.128897
91. EyeNet++: A Multiscale and Multidensity Approach for Outdoor 3-D Semantic Segmentation Inspired by the Human Visual Field S. Yoo et al. https://doi.org/10.1109/TGRS.2025.3589287
92. New Technology on 3D Point Cloud by Laser Measurement of Various Platforms T. SATOH et al. https://doi.org/10.2493/jjspe.85.223
93. A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation S. González-Collazo et al. https://doi.org/10.1016/j.eswa.2023.120672
94. Prospects and Challenges with Legal Informatics and Legal Metrology Framework in the Context of Industry 6.0 S. Chourasia et al. https://doi.org/10.1007/s12647-023-00664-8
95. Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey R. Zhang et al. https://doi.org/10.3390/electronics12173642
96. 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
97. A global point cloud semantic mapping method for underground spaces under extreme spatial constraints J. Hou et al. https://doi.org/10.1016/j.eswa.2026.133731
98. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al. https://doi.org/10.1007/s40725-024-00228-2
99. An Improved Explainable Point Cloud Classifier (XPCC) N. Arnold et al. https://doi.org/10.1109/TAI.2022.3150647
100. DCNet: Large-Scale Point Cloud Semantic Segmentation With Discriminative and Efficient Feature Aggregation F. Yin et al. https://doi.org/10.1109/TCSVT.2023.3239541
101. Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching M. Lalak & D. Wierzbicki https://doi.org/10.1109/JSTARS.2022.3149105
102. A Comprehensive Survey on Multi-View Classification: Methods, Applications, and Challenges K. Berahmand et al. https://doi.org/10.1145/3767728
103. Characterization and Analysis of Deep Learning for 3D Point Cloud Analytics B. Hyun et al. https://doi.org/10.1109/LCA.2021.3099117
104. OpenMPD: An Open Multimodal Perception Dataset for Autonomous Driving X. Zhang et al. https://doi.org/10.1109/TVT.2022.3143173
105. 3D Lidar Point Cloud Segmentation for Automated Driving R. Abbasi et al. https://doi.org/10.1109/MITS.2023.3325854
106. A Comprehensive Review of Vision-Based 3D Reconstruction Methods L. Zhou et al. https://doi.org/10.3390/s24072314
107. Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles Y. Xu et al. https://doi.org/10.1109/JSTARS.2018.2817227
108. Status, advancements and prospects of deep learning methods applied in forest studies T. Yun et al. https://doi.org/10.1016/j.jag.2024.103938
109. A network-level sidewalk inventory method using mobile LiDAR and deep learning Q. Hou & C. Ai https://doi.org/10.1016/j.trc.2020.102772
110. Transferable Adversarial Attacks on 3-D Point Cloud Semantic Segmentation via Diffusion Models in Autonomous Driving Y. Wang et al. https://doi.org/10.1109/TCE.2026.3673943
111. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini https://doi.org/10.4995/var.2021.15318
112. A Survey of Methods for Constructing 3D Urban Models From Point Clouds C. Romanengo et al. https://doi.org/10.1111/cgf.70312
113. CRBeDaSet: A Benchmark Dataset for High Accuracy Close Range 3D Object Reconstruction G. Gabara & P. Sawicki https://doi.org/10.3390/rs15041116
114. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. https://doi.org/10.1016/j.jag.2023.103566
115. Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B Y. Lian et al. https://doi.org/10.1109/JSTARS.2020.3035274
116. Semantic Segmentation for Point Clouds via Semantic-Based Local Aggregation and Multi-Scale Global Pyramid S. Cao et al. https://doi.org/10.3390/machines11010011
117. Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net Y. Wang & K. Koo https://doi.org/10.3390/app12010395
118. Hybrid deep learning and geometric algorithms for individual object detection in urban LiDAR point clouds R. Maskeliūnas et al. https://doi.org/10.1080/01431161.2025.2575517
119. Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation Y. Xie et al. https://doi.org/10.1109/MGRS.2019.2937630
120. BushNet: Effective semantic segmentation of bush in large-scale point clouds H. Wei et al. https://doi.org/10.1016/j.compag.2021.106653
121. Learning local contextual features for 3D point clouds semantic segmentation by attentive kernel convolution G. Tong et al. https://doi.org/10.1007/s00371-023-02819-9
122. Hybrid-Feature Geometric Descriptor for Point Clouds Registration in Architectural Scenarios J. Zhang et al. https://doi.org/10.1109/TGRS.2025.3628270
123. 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
124. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM) Y. Cai et al. https://doi.org/10.3390/rs13071367
125. Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Z. Guo & C. Feng https://doi.org/10.1080/13658816.2018.1552790
126. Machine Learning Generalisation across Different 3D Architectural Heritage E. Grilli & F. Remondino https://doi.org/10.3390/ijgi9060379
127. A Local-Global Feature Fusing Method for Point Clouds Semantic Segmentation Y. Bi et al. https://doi.org/10.1109/ACCESS.2023.3293161
128. Spatial Feature-Based Prioritization for Transmission of Point Cloud Data in 3D-Image Sensor Networks M. Oka et al. https://doi.org/10.1109/JSEN.2021.3106963
129. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds R. Huang et al. https://doi.org/10.1016/j.isprsjprs.2021.04.017
130. 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
131. Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing Z. Ding et al. https://doi.org/10.3390/robotics12040100
132. Determination of clay-water contact angle via molecular dynamics and deep-learning enhanced methods X. Song & Z. Zhang https://doi.org/10.1007/s11440-021-01238-1
133. MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification Y. Li et al. https://doi.org/10.3390/rs14225707
134. LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas Z. Ye et al. https://doi.org/10.3390/ijgi9070450
135. SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds Q. Hu et al. https://doi.org/10.1007/s11263-021-01554-9
136. A review on artificial intelligence applications for facades A. Duran et al. https://doi.org/10.1016/j.buildenv.2024.112310
137. A review of three dimensional reconstruction techniques J. Phang et al. https://doi.org/10.1007/s11042-021-10605-9
138. Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods K. Turgut et al. https://doi.org/10.1186/s13007-022-00857-3
139. Deep learning based computer vision under the prism of 3D point clouds: a systematic review K. Tychola et al. https://doi.org/10.1007/s00371-023-03237-7
140. A training dataset for semantic segmentation of urban point cloud map for intelligent vehicles H. Song et al. https://doi.org/10.1016/j.isprsjprs.2022.02.007
141. TPDNet: A point cloud data denoising method for offshore drilling platforms and its application C. Ran et al. https://doi.org/10.1016/j.measurement.2024.115671
142. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies A. Klimkowska et al. https://doi.org/10.3390/rs14112579
143. Viewpoint Recommendation for Point Cloud Labeling Through Interaction Cost Modeling Y. Zhang et al. https://doi.org/10.1109/TVCG.2024.3376951
144. DPC-Net: Distributed Point Convolution Network for large-scale point clouds semantic segmentation Y. Shao et al. https://doi.org/10.12688/cobot.17468.1
145. SyS3DS: Systematic Sampling of Large-Scale LiDAR Point Clouds for Semantic Segmentation in Forestry Robotics H. Mukhandi et al. https://doi.org/10.3390/s24030823
146. Learning From Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation E. Camuffo et al. https://doi.org/10.1109/TMM.2023.3345152
147. MS-RRFSegNet: Multiscale Regional Relation Feature Segmentation Network for Semantic Segmentation of Urban Scene Point Clouds H. Luo et al. https://doi.org/10.1109/TGRS.2020.2985695
148. Truncated octree and its applications N. Koh et al. https://doi.org/10.1007/s00371-021-02130-5
149. A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds R. Zhang et al. https://doi.org/10.1016/j.jag.2021.102411
150. Deep Learning Based Point Cloud Processing Techniques A. Hazer & R. Yildirim https://doi.org/10.1109/ACCESS.2022.3226211
151. GRASS: Glass Reflection Artifact Suppression Strategy via Virtual Point Removal in LiDAR Point Clouds W. Shao et al. https://doi.org/10.3390/rs18020332
152. Triple Point Masking J. Liu et al. https://doi.org/10.1109/TCSVT.2025.3549786
153. GMFE-Net: point cloud semantic segmentation with general multi-feature fusion and extraction T. Li et al. https://doi.org/10.1080/01431161.2025.2603691
154. A Simple Semantic-Based Data Storage Layout for Querying Point Clouds S. El-Mahgary et al. https://doi.org/10.3390/ijgi9020072
155. An efficient global energy optimization approach for robust 3D plane segmentation of point clouds Z. Dong et al. https://doi.org/10.1016/j.isprsjprs.2018.01.013
156. Object detection for automotive radar point clouds – a comparison N. Scheiner et al. https://doi.org/10.1186/s42467-021-00012-z
157. 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
158. DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar N. Singer & V. Asari https://doi.org/10.1109/ACCESS.2021.3094127
159. RAAFNet: Reverse Attention Adaptive Fusion Network for Large-Scale Point Cloud Semantic Segmentation K. Wang & H. Zhang https://doi.org/10.3390/math12162485
160. 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 https://doi.org/10.3390/rs16234466
161. Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications S. Lytkin et al. https://doi.org/10.3390/rs15112735
162. Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds Z. Xu et al. https://doi.org/10.3390/buildings12070968
163. Spatial Adaptive Fusion Consistency Contrastive Constraint: Weakly Supervised Building Facade Point Cloud Semantic Segmentation Y. Su et al. https://doi.org/10.1109/TGRS.2023.3297631
164. A local tangent plane distance-based approach to 3D point cloud segmentation via clustering H. Chen et al. https://doi.org/10.1016/j.patcog.2023.109307
165. A Deep Neural Network Using Double Self-Attention Mechanism for ALS Point Cloud Segmentation L. Yu et al. https://doi.org/10.1109/ACCESS.2022.3158438
166. A Novel Deep Learning-Based Approach for Rift and Iceberg Recognition From ICESat-2 Data Z. Huang et al. https://doi.org/10.1109/TGRS.2024.3382573
167. Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors L. Zhang et al. https://doi.org/10.1109/TGRS.2021.3091380
168. Exploiting 2D/3D Geomatics Data for the Management, Promotion, and Valorization of Underground Built Heritage L. Gorgoglione et al. https://doi.org/10.3390/smartcities6010012
169. FG-Net: A Fast and Accurate Framework for Large-Scale LiDAR Point Cloud Understanding K. Liu et al. https://doi.org/10.1109/TCYB.2022.3159815
170. A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation K. Zhang et al. https://doi.org/10.1109/TGRS.2022.3201902
171. Efficient multi-modal high-precision semantic segmentation from MLS point cloud without 3D annotation Y. Wang et al. https://doi.org/10.1016/j.jag.2024.104243
172. Pyramid Point: A Multilevel Focusing Network for Revisiting Feature Layers N. Varney & V. Asari https://doi.org/10.1109/LGRS.2022.3191743
173. PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation J. Zhang et al. https://doi.org/10.1109/TPAMI.2025.3562051
174. Learning-based robotic grasping: A review Z. Xie et al. https://doi.org/10.3389/frobt.2023.1038658
175. STSD:A large-scale benchmark for semantic segmentation of subway tunnel point cloud H. Cui et al. https://doi.org/10.1016/j.tust.2024.105829
176. Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information H. Fang & F. Lafarge https://doi.org/10.1016/j.isprsjprs.2019.06.010
177. ITU-Campus3D: A Campus-Scale Urban Mobile Mapping Benchmark for Semantic Segmentation K. Aksu et al. https://doi.org/10.1109/ACCESS.2026.3682205
178. A large-scale point cloud semantic segmentation neural network based on long-range contextual dependencies enhancement H. Zhang et al. https://doi.org/10.1080/2150704X.2024.2343131
179. Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Z. Ballouch et al. https://doi.org/10.3390/rs16020329
180. Deep Learning-Based 3D Point Cloud Segmentation for Nondestructive Evaluation and Monitoring of Tunnel Construction L. Kou et al. https://doi.org/10.1007/s10921-025-01293-8
181. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al. https://doi.org/10.3390/rs10111723
182. Few-Shot 3D Point Cloud Segmentation via Relation Consistency-Guided Heterogeneous Prototypes L. Wei et al. https://doi.org/10.1109/TMM.2025.3557699
183. BSTS: A Weakly-Supervised Method for Semantic Learning of 3D Point Clouds Y. Liu et al. https://doi.org/10.1109/TCSVT.2024.3420150
184. LiDAR remote sensing meets weak supervision: Concepts, methods, and perspectives Y. Gao et al. https://doi.org/10.1016/j.isprsjprs.2026.03.004
185. Large-Scale Point Cloud Contour Extraction via 3-D-Guided Multiconditional Residual Generative Adversarial Network Y. Zhang et al. https://doi.org/10.1109/LGRS.2019.2917319
186. 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
187. The Mixed Kernel Function SVM-Based Point Cloud Classification C. Chen et al. https://doi.org/10.1007/s12541-019-00102-3
188. BubblEX: An Explainable Deep Learning Framework for Point-Cloud Classification F. Matrone et al. https://doi.org/10.1109/JSTARS.2022.3195200
189. Classification and Change Detection in Mobile Mapping LiDAR Point Clouds M. Voelsen et al. https://doi.org/10.1007/s41064-021-00148-x
190. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage R. Pierdicca et al. https://doi.org/10.3390/rs12061005
191. Urban vegetation segmentation using terrestrial LiDAR point clouds based on point non-local means network Y. Chen et al. https://doi.org/10.1016/j.jag.2021.102580
192. Out-of-core outlier removal for large-scale indoor point clouds L. Ge & J. Feng https://doi.org/10.1016/j.gmod.2022.101142
193. Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks S. Wang et al. https://doi.org/10.1038/s41598-024-71989-w
194. Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor D. Chen et al. https://doi.org/10.3390/rs13163146
195. 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
196. Object recognition datasets and challenges: A review A. Salari et al. https://doi.org/10.1016/j.neucom.2022.01.022
197. A Multiscale Convolutional Neural Network With Color Vegetation Indices for Semantic Labeling of Point Cloud H. Zhang et al. https://doi.org/10.1109/LGRS.2021.3079242
198. Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview E. Camuffo et al. https://doi.org/10.3390/s22041357
199. An Improved RandLa-Net Algorithm Incorporated with NDT for Automatic Classification and Extraction of Raw Point Cloud Data Z. Ma et al. https://doi.org/10.3390/electronics11172795
200. A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++ K. Chang et al. https://doi.org/10.3390/agriculture14050720
201. Evaluating the Quality of Semantic Segmented 3D Point Clouds E. Barnefske & H. Sternberg https://doi.org/10.3390/rs14030446
202. RdmkNet & Toronto-RDMK: Large-Scale Datasets for Road Marking Classification and Segmentation J. Du et al. https://doi.org/10.1109/TITS.2024.3394481
203. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al. https://doi.org/10.3390/s22207868
204. Quantity-Quality Enhanced Self-Training Network for Weakly Supervised Point Cloud Semantic Segmentation J. Deng et al. https://doi.org/10.1109/TPAMI.2025.3532637
205. 3D line segment extraction for architectural facades via neighbor weighted local centroid and slicing strategy Y. He et al. https://doi.org/10.1080/10095020.2025.2591258
206. A Graph-Voxel Joint Convolution Neural Network for ALS Point Cloud Segmentation J. Zhang et al. https://doi.org/10.1109/ACCESS.2020.3013293
207. Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review Y. Li et al. https://doi.org/10.1109/TNNLS.2020.3015992
208. WHU-Urban3D: An urban scene LiDAR point cloud dataset for semantic instance segmentation X. Han et al. https://doi.org/10.1016/j.isprsjprs.2024.02.007
209. An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation M. Huang et al. https://doi.org/10.3390/rs11232727
210. TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas J. Zhu et al. https://doi.org/10.3390/rs12111875
211. A Robust Automatic Method to Extract Building Facade Maps from 3D Point Cloud Data B. Yu et al. https://doi.org/10.3390/rs14163848
212. Deep Learning-Based Damage Detection from Aerial SfM Point Clouds M. Mohammadi et al. https://doi.org/10.3390/drones3030068
213. Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method A. Dutta et al. https://doi.org/10.1109/TPAMI.2018.2869744
214. Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment Y. Liao et al. https://doi.org/10.3390/drones4020024
215. Boundary–Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation L. He et al. https://doi.org/10.3390/app13064053
216. Fast and Lite Point Cloud Semantic Segmentation for Autonomous Driving Utilizing LiDAR Synthetic Training Data J. Jeong et al. https://doi.org/10.1109/ACCESS.2022.3184803
217. Building facade structure extraction method based on three-dimensional laser point cloud by considering semantic information X. Hu et al. https://doi.org/10.1080/19475683.2024.2335953
218. AI-Driven Landing Zone Detection Module for Vertical Take-Off and Landing Vehicles Using Projection-Based LiDAR-Navigation Pipelines N. Herath et al. https://doi.org/10.1109/TASE.2025.3563822
219. VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments K. Saleh et al. https://doi.org/10.1109/TITS.2023.3271414
220. Semi-automated Cleaning of Laser Scanning Campaigns with Machine Learning P. Marais et al. https://doi.org/10.1145/3292027
221. 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
222. A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass M. Yang et al. https://doi.org/10.3390/f13040616
223. Efficient 3D Scene Semantic Segmentation via Active Learning on Rendered 2D Images M. Rong et al. https://doi.org/10.1109/TIP.2023.3286708
224. A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm H. Zhang et al. https://doi.org/10.3390/rs15143480
225. Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis D. Schunck et al. https://doi.org/10.1371/journal.pone.0256340
226. A New Open-Source Software to Help Design Models for Automatic 3D Point Cloud Classification in Coastal Studies X. Pellerin Le Bas et al. https://doi.org/10.3390/rs16162891
227. An Efficient Hypergraph Approach to Robust Point Cloud Resampling Q. Deng et al. https://doi.org/10.1109/TIP.2022.3149225
228. A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds Z. Sha et al. https://doi.org/10.1109/LGRS.2020.3037484
229. Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving R. Abbasi et al. https://doi.org/10.1109/TITS.2022.3167957
230. Discrimination of Plant Structures in 3D Point Cloud Through Back-Projection of Labels Derived from 2D Semantic Segmentation T. Imabuchi & K. Kawabata https://doi.org/10.20965/jrm.2024.p0063
231. Intensity-Assisted ICP for Fast Registration of 2D-LIDAR Y. Tian et al. https://doi.org/10.3390/s19092124
232. 3-D Object Classification in Heterogeneous Point Clouds via Bag-of-Words and Joint Distribution Adaption H. Luo et al. https://doi.org/10.1109/LGRS.2019.2911200
233. Explicit Incorporation of Spatial Autocorrelation in 3D Deep Learning for Geospatial Object Detection T. Chen et al. https://doi.org/10.1080/24694452.2024.2380898
234. AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation W. Jing et al. https://doi.org/10.3390/rs14041036
235. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation F. Matrone et al. https://doi.org/10.3390/ijgi9090535
236. Computer Vision for Autonomous Vehicles J. Janai et al. https://doi.org/10.1561/0600000079
237. Juggling with representations: On the information transfer between imagery, point clouds, and meshes for multi-modal semantics D. Laupheimer & N. Haala https://doi.org/10.1016/j.isprsjprs.2021.03.007
238. HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation C. Wen et al. https://doi.org/10.1007/s10462-025-11111-2
239. A survey on end‐to‐end point cloud learning X. Tang et al. https://doi.org/10.1049/ipr2.12729
240. WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation B. Qiu et al. https://doi.org/10.1109/TITS.2024.3469546
241. Instance Segmentation of Industrial Point Cloud Data E. Agapaki & I. Brilakis https://doi.org/10.1061/(ASCE)CP.1943-5487.0000972
242. MS-SADNet: A Multi-Scale Self-Attention Decoder Network for Point Cloud Semantic Segmentation Y. ZHOU et al. https://doi.org/10.1587/transinf.2025EDP7214
243. PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation Q. Li et al. https://doi.org/10.1109/TVCG.2025.3638450
244. Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots T. Miao et al. https://doi.org/10.1093/gigascience/giab031
245. MSIDA-Net: Point Cloud Semantic Segmentation via Multi-Spatial Information and Dual Adaptive Blocks F. Shuang et al. https://doi.org/10.3390/rs14092187
246. Optimizing rural waste management: Leveraging high-resolution remote sensing and GIS for efficient collection and routing X. Cheng et al. https://doi.org/10.1016/j.jag.2024.104219
247. Evaluating Deep Learning Advances for Point Cloud Semantic Segmentation in Urban Environments H. Yan et al. https://doi.org/10.1007/s42489-025-00185-1
248. PU-FPG: Point cloud upsampling via form preserving graph convolutional networks H. Wang et al. https://doi.org/10.3233/JIFS-232490
249. 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
250. LiDAR Data Augmentation for Semantic Segmentation: A Statistical Intensity Model P. Aguilar-Pérez & R. Díaz-Redondo https://doi.org/10.12720/jait.16.2.223-232
251. Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods B. Gao et al. https://doi.org/10.1109/TITS.2021.3076844
252. Deep learning framework for point cloud instance segmentation in construction sites H. Yue et al. https://doi.org/10.1016/j.autcon.2026.107159
253. Designing and Building an Intelligent Pavement Management System for Urban Road Networks M. Moradi & G. Assaf https://doi.org/10.3390/su15021157
254. Generation of Thermal Point Clouds From Uncalibrated Thermal Infrared Image Sequences and Mobile Laser Scans J. Zhu et al. https://doi.org/10.1109/TIM.2023.3284942
255. A GPU-Accelerated Framework for Simulating LiDAR Scanning A. Lopez et al. https://doi.org/10.1109/TGRS.2022.3165746
256. QoS Awareness and Improved Throughput of Point Cloud Services With Dynamic Workloads K. Fu et al. https://doi.org/10.1109/TC.2025.3648132
257. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation S. Sarker et al. https://doi.org/10.1007/s00138-024-01543-1
258. QuickFPS: Architecture and Algorithm Co-Design for Farthest Point Sampling in Large-Scale Point Clouds M. Han et al. https://doi.org/10.1109/TCAD.2023.3274922
259. 3D point cloud descriptors: state-of-the-art X. Han et al. https://doi.org/10.1007/s10462-023-10486-4
260. Robust point cloud normal estimation via multi-level critical point aggregation J. Zhou et al. https://doi.org/10.1007/s00371-024-03532-x
261. 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
262. Color guided convolutional network for point cloud semantic segmentation J. Yang et al. https://doi.org/10.1177/17298806221098506
263. 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
264. SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints P. Testolina et al. https://doi.org/10.1109/TITS.2023.3257086
265. A dataset of multi-modal UAV remote sensing data for semantic segmentation of open-pit coal mines P. CUI et al. https://doi.org/10.11922/11-6035.csd.2025.0100.zh
266. Point cloud features suitable for automatic labeling of MMS point cloud data G. TAKAHASHI & H. MASUDA https://doi.org/10.4287/jsprs.60.266
267. Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues N. Qin et al. https://doi.org/10.1016/j.isprsjprs.2023.06.005
268. Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds H. Lei et al. https://doi.org/10.1109/TPAMI.2020.2983410
269. A comprehensive review on application of drone, virtual reality and augmented reality with their application in dragline excavation monitoring in surface mines P. Singh et al. https://doi.org/10.1080/19475705.2024.2327399
270. RESEARCH FOR JUDGING ROAD BLOCKAGE CAUSED BY COLLAPSED BLOCK WALLS USING POINT CLOUD DATA Y. UMEHARA et al. https://doi.org/10.2208/jscejj.22-22019
271. Towards a Meaningful 3D Map Using a 3D Lidar and a Camera J. Jeong et al. https://doi.org/10.3390/s18082571
272. Data Preparation Impact on Semantic Segmentation of 3D Mobile LiDAR Point Clouds Using Deep Neural Networks R. Mahmoudi Kouhi et al. https://doi.org/10.3390/rs15040982
273. A survey on deep learning fundamentals C. Tian et al. https://doi.org/10.1007/s10462-025-11368-7
274. Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++ A. Esmoris et al. https://doi.org/10.1109/ACCESS.2022.3211072
275. Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset J. Behley et al. https://doi.org/10.1177/02783649211006735
276. Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping J. Deschaud et al. https://doi.org/10.3390/rs13224713
277. Vo-Norvana : Versatile Framework for Efficient Segmentation of Large Point Cloud Data Sets E. Che & M. Olsen https://doi.org/10.1061/JCCEE5.CPENG-4979
278. IB-PC: An Information Bottleneck Framework for Point Cloud-Based Building Information Modeling Y. Zhang et al. https://doi.org/10.3390/electronics14224399
279. A Survey of Label-Efficient Deep Learning for 3D Point Clouds A. Xiao et al. https://doi.org/10.1109/TPAMI.2024.3416302
280. Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition N. Qin et al. https://doi.org/10.1016/j.isprsjprs.2018.03.011
281. PointMCD: Boosting Deep Point Cloud Encoders via Multi-View Cross-Modal Distillation for 3D Shape Recognition Q. Zhang et al. https://doi.org/10.1109/TMM.2023.3286981
282. A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation Z. Yang et al. https://doi.org/10.3390/s24227222
283. Hierarchical Weight Averaging for Deep Neural Networks X. Gu et al. https://doi.org/10.1109/TNNLS.2023.3255540
284. Thiruvananthapuram Aerial LiDAR Dataset (TALD): A Benchmark for Complex Urban Point Cloud J. Vijaywargiya & A. Ramiya https://doi.org/10.1109/ACCESS.2025.3546628
285. Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor M. Ibrahim et al. https://doi.org/10.3390/rs13183621
286. A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data W. Li et al. https://doi.org/10.1109/TGRS.2021.3076107
287. UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes H. Sheng et al. https://doi.org/10.1109/TCSVT.2022.3187664
288. MFFRand: Semantic Segmentation of Point Clouds Based on Multi-Scale Feature Fusion and Multi-Loss Supervision Z. Miao et al. https://doi.org/10.3390/electronics11213626
289. 2D–3D fusion approach for improved point cloud segmentation H. Yue et al. https://doi.org/10.1016/j.autcon.2025.106336
290. 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/geomat-2018-0001
291. 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
292. A multi-granularity semisupervised active learning for point cloud semantic segmentation S. Ye et al. https://doi.org/10.1007/s00521-023-08455-7
293. Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data X. Wei et al. https://doi.org/10.1109/TITS.2024.3461735
294. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al. https://doi.org/10.3390/rs13101985
295. 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
296. A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds Q. Chen et al. https://doi.org/10.1016/j.jag.2022.102974
297. One Class One Click: Quasi scene-level weakly supervised point cloud semantic segmentation with active learning P. Wang et al. https://doi.org/10.1016/j.isprsjprs.2023.09.002
298. Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis Y. Zhang et al. https://doi.org/10.3390/rs13061136
299. PReFormer: A memory-efficient transformer for point cloud semantic segmentation P. Akwensi et al. https://doi.org/10.1016/j.jag.2024.103730
300. Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network H. Hu et al. https://doi.org/10.1080/17538947.2024.2310083
301. A multi-task network for building massing reconstruction based on a single image in early-stage design X. Xu et al. https://doi.org/10.1016/j.aei.2026.105054
302. MuSic-UDF: Learning Multi-Scale dynamic grid representation for high-fidelity surface reconstruction from point clouds C. Jin et al. https://doi.org/10.1016/j.cag.2024.104081
303. Superpoint Transformer–Based Bridge Component Recognition Using UAV-Mounted LiDAR and Synthetic Point Cloud Generation Y. Kim et al. https://doi.org/10.1061/JCCEE5.CPENG-6383
304. Deep learning-based fast prediction of flow field around multiple bluff bodies P. Lin et al. https://doi.org/10.1016/j.awe.2024.100020
305. Semantic Context Encoding for Accurate 3D Point Cloud Segmentation H. Liu et al. https://doi.org/10.1109/TMM.2020.3007331
306. An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation M. Chen et al. https://doi.org/10.3390/rs16224240
307. FFA-Net: fast feature aggregation network for 3D point cloud segmentation R. Cheng et al. https://doi.org/10.1007/s00138-023-01434-x
308. Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network W. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2020.04.003
309. ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation J. Du et al. https://doi.org/10.1016/j.isprsjprs.2021.09.024
310. Optimal LiDAR Data Resolution Analysis for Object Classification M. Darrah et al. https://doi.org/10.3390/s22145152
311. MVF-CNN: Fusion of Multilevel Features for Large-Scale Point Cloud Classification Y. Li et al. https://doi.org/10.1109/ACCESS.2019.2908983
312. 3D Semantic Scene Completion: A Survey L. Roldão et al. https://doi.org/10.1007/s11263-021-01504-5
313. Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning H. Luo et al. https://doi.org/10.1109/LSP.2025.3557289
314. Lidar Mapping Optimization Based on Lightweight Semantic Segmentation Z. Zhao et al. https://doi.org/10.1109/TIV.2019.2919432
315. 基于最小平方中值的点云平面拟合算法 王. Wang Yang et al. https://doi.org/10.3788/LOP213208
316. Automated verticality quality assessment in prefabrication construction using laser scanning and synthetic point cloud data M. Wang et al. https://doi.org/10.1016/j.jobe.2026.115252
317. Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks A. Godo et al. https://doi.org/10.1109/ACCESS.2022.3156108
318. 3DGENie: Synthetic point clouds for semantic segmentation in realistic virtual environments A. Yaghi et al. https://doi.org/10.1007/s11042-025-20973-1
319. Mesh Convolution With Continuous Filters for 3-D Surface Parsing H. Lei et al. https://doi.org/10.1109/TNNLS.2023.3281871
320. Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey L. Rauch & T. Braml https://doi.org/10.3390/app13169146
321. IGN: Instance-Guided Net for 3-D Instance Segmentation in Cluttered Scenes via Monocular Depth Sensor Z. Wan et al. https://doi.org/10.1109/JSEN.2024.3416149
322. A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation M. Kamal et al. https://doi.org/10.1145/3815180
323. Graph Neural Networks in Point Clouds: A Survey D. Li et al. https://doi.org/10.3390/rs16142518
324. SVLA: A compact supervoxel segmentation method based on local allocation H. Ni & X. Niu https://doi.org/10.1016/j.isprsjprs.2020.03.011
325. Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators T. Shinohara et al. https://doi.org/10.1109/JSTARS.2021.3124610
326. RR-Net: 3-D Roof Reconstruction From Airborne LiDAR Point Clouds via Edge Segmentation and Wireframe Generation Y. Xu et al. https://doi.org/10.1109/TGRS.2025.3625669
327. 3D point cloud processing and analysis: a survey A. Dehghanpour et al. https://doi.org/10.1007/s11042-026-21797-3
328. Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review S. Yang et al. https://doi.org/10.3390/rs15030548
329. Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration A. Zaganidis et al. https://doi.org/10.1109/LRA.2018.2848308
330. Hybrid Local-Feature-Based Approach for Automated Rail Extraction from Lidar Data Y. Ren et al. https://doi.org/10.1177/03611981241311575
331. 基于点‑体素一致性约束的城市激光雷达点云分类 李. Li Huchen et al. https://doi.org/10.3788/CJL231411
332. Deep Learning-Based Semantic Segmentation and Surface Reconstruction for Point Clouds of Offshore Oil Production Equipment Z. Wang et al. https://doi.org/10.1109/TGRS.2023.3348797
333. PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification G. Zhao et al. https://doi.org/10.3390/rs13214312
334. NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer X. Hu et al. https://doi.org/10.3390/s22145128
335. Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation M. Ibrahim et al. https://doi.org/10.1109/ACCESS.2021.3062547
336. 3D Point Cloud Dataset of Heavy Construction Equipment S. Park & S. Kim https://doi.org/10.3390/app14093599
337. 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
338. Positive-Negative Receptive Field Reasoning for Omni-Supervised 3D Segmentation X. Tan et al. https://doi.org/10.1109/TPAMI.2023.3319470
339. PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application J. Li et al. https://doi.org/10.1155/2022/9433661
340. Backward Attentive Fusing Network With Local Aggregation Classifier for 3D Point Cloud Semantic Segmentation H. Shuai et al. https://doi.org/10.1109/TIP.2021.3073660
341. Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations Y. Cai et al. https://doi.org/10.1109/TGRS.2022.3161982
342. Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines H. Yu et al. https://doi.org/10.3390/rs15092371
343. CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation G. Tong et al. https://doi.org/10.1109/ACCESS.2020.2992612
344. Neighborhood co-occurrence modeling in 3D point cloud segmentation J. Gong et al. https://doi.org/10.1007/s41095-021-0244-6
345. A Comprehensive Review of Modern Object Segmentation Approaches Y. Wang et al. https://doi.org/10.1561/0600000097
346. Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification I. Farmakis et al. https://doi.org/10.3390/rs13071354
347. Deep Learning on 3D Semantic Segmentation: A Detailed Review T. Betsas et al. https://doi.org/10.3390/rs17020298
348. A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models X. Yao et al. https://doi.org/10.1007/s10845-022-01939-8
349. 3D CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data B. Nagy & C. Benedek https://doi.org/10.1109/JSEN.2019.2927269
350. Distributed Optimization Based on Graph Filter for Ensuring Invariant Simplification of High-Volume Point Cloud X. Zhou et al. https://doi.org/10.1109/TCE.2023.3272773
351. Using Relative Projection Density for Classification of Terrestrial Laser Scanning Data with Unknown Angular Resolution M. Chen et al. https://doi.org/10.3390/rs14236043
352. One Shot Learning for Edge Detection on Point Clouds Z. Tu et al. https://doi.org/10.1109/TVCG.2025.3542475
353. Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows S. Xia & R. Wang https://doi.org/10.1109/JSTARS.2019.2897987
354. PointGrad: A Unified Spatially Varying Tensor Gradient Operator for Point Cloud Segmentation R. Bao et al. https://doi.org/10.1109/TGRS.2026.3686390
355. Automated and Fast Point Cloud Labeling Method for Training Data Augmentation Using Scan-versus-BIM Approach S. Park et al. https://doi.org/10.1061/JCCEE5.CPENG-7153
356. A Survey of Synthetic Data Augmentation Methods in Machine Vision A. Mumuni et al. https://doi.org/10.1007/s11633-022-1411-7
357. Hausdorff point convolution with geometric priors L. Lin et al. https://doi.org/10.1007/s11432-021-3311-2
358. Deep Learning for Generating Time-of-Flight Camera Artifacts T. Müller et al. https://doi.org/10.3390/jimaging10100246
359. A Review on Deep Learning Techniques for 3D Sensed Data Classification D. Griffiths & J. Boehm https://doi.org/10.3390/rs11121499
360. SegTrans: Semantic Segmentation With Transfer Learning for MLS Point Clouds S. Shen et al. https://doi.org/10.1109/LGRS.2023.3294748
361. Designing and Testing a UAV Mapping System for Agricultural Field Surveying M. Christiansen et al. https://doi.org/10.3390/s17122703
362. Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation C. Liu et al. https://doi.org/10.1109/TGRS.2023.3339475
363. Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation X. Geng et al. https://doi.org/10.3390/rs13040691
364. Weakly supervised point cloud segmentation via deep morphological semantic information embedding W. Xue et al. https://doi.org/10.1049/cit2.12239
365. Efficient Semantic Segmentation of Transmission Line Corridor Point Clouds via Transformer With Vector Aggregation H. Hu et al. https://doi.org/10.1109/JSTARS.2026.3662897
366. GL-Net: Semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation J. Li et al. https://doi.org/10.1016/j.isprsjprs.2023.04.011
367. Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models T. Zoumpekas et al. https://doi.org/10.3390/rs14236049
368. Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds Y. Xiao et al. https://doi.org/10.3390/rs12030473
369. Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations M. Sánchez et al. https://doi.org/10.3390/s22155599
370. Shield Tunnel Dislocation Detection Method Based on Semantic Segmentation and Bolt Hole Positioning of MLS Point Cloud H. Cui et al. https://doi.org/10.1109/TGRS.2024.3386308
371. Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning K. Shang et al. https://doi.org/10.1016/j.jmsy.2023.10.017
372. Low-Overlap Registration of Multi-Source LiDAR Point Clouds in Urban Scenes Through Dual-Stage Feature Pruning and Progressive Hierarchical Methods K. Ma et al. https://doi.org/10.3390/rs17172938
373. An Accurate and Efficient Supervoxel Re-Segmentation Approach for Large-Scale Point Clouds Using Plane Constraints B. Lai et al. https://doi.org/10.3390/rs15163973
374. Semantics for Robotic Mapping, Perception and Interaction: A Survey S. Garg et al. https://doi.org/10.1561/2300000059
375. KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D Y. Liao et al. https://doi.org/10.1109/TPAMI.2022.3179507
376. Context-Aware 3D Point Cloud Semantic Segmentation With Plane Guidance T. Weng et al. https://doi.org/10.1109/TMM.2022.3212914
377. Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds H. Luo et al. https://doi.org/10.1016/j.jag.2021.102564
378. RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging K. Liu https://doi.org/10.1007/s11263-022-01740-3
379. Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village V. Cotella et al. https://doi.org/10.1080/09613218.2025.2521751
380. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation F. Matrone et al. https://doi.org/10.1080/17538947.2024.2390457
381. PointDMS: An Improved Deep Learning Neural Network via Multi-Feature Aggregation for Large-Scale Point Cloud Segmentation in Smart Applications of Urban Forestry Management J. Li & J. Liu https://doi.org/10.3390/f14112169
382. Deep Learning for 3D Point Clouds: A Survey Y. Guo et al. https://doi.org/10.1109/TPAMI.2020.3005434
383. Deep learning techniques for point cloud tasks: a review X. Song et al. https://doi.org/10.1007/s10489-025-06854-y
384. Review: Deep Learning on 3D Point Clouds S. Bello et al. https://doi.org/10.3390/rs12111729
385. Multilevel Ground Segmentation for 3-D Point Clouds of Outdoor Scenes Based on Shape Analysis Y. An et al. https://doi.org/10.1109/TIM.2022.3154835
386. Context-Aware RandLA-Net: An Enhanced Architecture for Large-Scale Point Cloud Semantic Segmentation J. Sun et al. https://doi.org/10.1109/JIOT.2026.3655384
387. A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation J. Han et al. https://doi.org/10.1109/TIP.2024.3372446
388. Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework Z. Zeng et al. https://doi.org/10.1016/j.jag.2024.104309
389. Semantic segmentation of large-scale point clouds with neighborhood uncertainty Y. Bao et al. https://doi.org/10.1007/s11042-023-17814-4
390. Towards Urban Scene Semantic Segmentation with Deep Learning from LiDAR Point Clouds: A Case Study in Baden-Württemberg, Germany Y. Zou et al. https://doi.org/10.3390/rs13163220
391. Nested Architecture Search for Point Cloud Semantic Segmentation F. Yang et al. https://doi.org/10.1109/TIP.2022.3147983
392. RESEARCH ON CREATION OF IDENTIFICATION MODELS OF ROAD FEATURES USING HD MAPS R. IMAI et al. https://doi.org/10.2208/jscejj.22-22022
393. Topology-aware multi-information fusion for object recognition Y. Wang et al. https://doi.org/10.1038/s41598-026-46632-5