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

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1. DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds L. Chen et al. 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 10.3390/rs10010002
3. Extraction of local structure information of point clouds through space-filling curve for semantic segmentation X. Xiang et al. 10.1016/j.jag.2022.103027
4. SFL-NET: Slight Filter Learning Network for Point Cloud Semantic Segmentation X. Li et al. 10.1109/TGRS.2023.3313876
5. Point Cloud Convolution Network Based on Spatial Location Correspondence J. Xv et al. 10.3390/ijgi11120591
6. 3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net J. Li et al. 10.1109/ACCESS.2020.3044166
7. Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-Supervised Learning J. Mei et al. 10.1109/TITS.2019.2919741
8. Assessing the visibility of urban greenery using MLS LiDAR data L. Tang et al. 10.1016/j.landurbplan.2022.104662
9. Deep-Learning-Based 3-D Surface Reconstruction—A Survey A. Farshian et al. 10.1109/JPROC.2023.3321433
10. Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds X. Zhang et al. 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. 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. 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 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. 10.1109/TITS.2021.3119132
15. CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets E. Agapaki & I. Brilakis 10.1016/j.aei.2020.101121
16. Semantic Segmentation and Reconstruction of Indoor Scene Point Clouds W. HAO et al. 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 10.26833/ijeg.709212
18. A multi-point focus transformer approach for large-scale ALS point cloud ground filtering T. Liu et al. 10.1080/01431161.2024.2443604
19. 3D point cloud semantic segmentation toward large-scale unstructured agricultural scene classification Y. Chen et al. 10.1016/j.compag.2021.106445
20. Multi-Feature Aggregation for Semantic Segmentation of an Urban Scene Point Cloud J. Chen et al. 10.3390/rs14205134
21. A Review of Deep Learning-Based Semantic Segmentation for Point Cloud J. Zhang et al. 10.1109/ACCESS.2019.2958671
22. Consistent Semantic Annotation of Outdoor Datasets via 2D/3D Label Transfer R. Tylecek & R. Fisher 10.3390/s18072249
23. Continuous conditional random field convolution for point cloud segmentation F. Yang et al. 10.1016/j.patcog.2021.108357
24. Incorporating Human Domain Knowledge in 3-D LiDAR-Based Semantic Segmentation J. Mei & H. Zhao 10.1109/TIV.2019.2955851
25. Rotation invariance and equivariance in 3D deep learning: a survey J. Fei & Z. Deng 10.1007/s10462-024-10741-2
26. Deep learning on multi-view sequential data: a survey Z. Xie et al. 10.1007/s10462-022-10332-z
27. Pseudo-labelling-aided semantic segmentation on sparsely annotated 3D point clouds Y. Yao et al. 10.1186/s41074-020-00064-w
28. Revisiting the Effectiveness of 3D Object Recognition Benchmarks H. Song & S. Lee 10.1109/ACCESS.2023.3322433
29. Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation From MLS A. Aditya et al. 10.1109/TGRS.2024.3381976
30. A Laplacian for Nonmanifold Triangle Meshes N. Sharp & K. Crane 10.1111/cgf.14069
31. Object-based incremental registration of terrestrial point clouds in an urban environment X. Ge & H. Hu 10.1016/j.isprsjprs.2020.01.020
32. Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences A. Afifi et al. 10.1109/TGRS.2023.3340293
33. SSGAM-Net: A Hybrid Semi-Supervised and Supervised Network for Robust Semantic Segmentation Based on Drone LiDAR Data H. Wu et al. 10.3390/rs16010092
34. Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes J. Han et al. 10.1109/TIP.2021.3106811
35. A Knowledge-Based Multi-Scale Adaptive Classification Approach for Mobile Laser Scanning Point Clouds in Urban Scenes M. Zheng et al. 10.1109/ACCESS.2023.3335607
36. A Survey of Point Cloud Completion Z. Zhuang et al. 10.1109/JSTARS.2024.3362476
37. GA-NET: Global Attention Network for Point Cloud Semantic Segmentation S. Deng & Q. Dong 10.1109/LSP.2021.3082851
38. Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış M. DEMİRTAŞ 10.24012/dumf.1067736
39. A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification Z. Wang et al. 10.1109/TGRS.2018.2829625
40. Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization W. Li et al. 10.1016/j.isprsjprs.2020.01.021
41. A shimming method based on outer mold line control of thin-walled parts S. Li et al. 10.1177/09544054221118795
42. Lidar Point Semantic Segmentation Using Dual Attention Mechanism H. Wang et al. 10.1007/s10946-023-10127-9
43. Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds S. González-Collazo et al. 10.1016/j.eswa.2023.121842
44. Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage W. Li et al. 10.3390/rs16152758
45. A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds L. Landrieu et al. 10.1016/j.isprsjprs.2017.08.010
46. Learning-Based Reflection-Aware Virtual Point Removal for Large-Scale 3D Point Clouds O. Lee et al. 10.1109/LRA.2023.3329365
47. PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest Environments J. Li et al. 10.3390/f14122276
48. Classification and Segmentation of Mining Area Objects in Large-Scale Spares Lidar Point Cloud Using a Novel Rotated Density Network Y. Yan et al. 10.3390/ijgi9030182
49. PECo: A Point-Edge Collaborative Framework for Global-Aware Urban Building Contouring From Unstructured Point Clouds S. Di et al. 10.1109/TGRS.2023.3342807
50. AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-Time High-Fidelity LiDAR Simulation J. Richa et al. 10.3390/rs14246262
51. The role of machine intelligence in photogrammetric 3D modeling – an overview and perspectives R. Qin & A. Gruen 10.1080/17538947.2020.1805037
52. Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry A. Kamran-Pishhesari et al. 10.3390/technologies12030040
53. PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry R. Zhang et al. 10.1021/acs.jctc.3c01420
54. DenseKPNET: Dense Kernel Point Convolutional Neural Networks for Point Cloud Semantic Segmentation Y. Li et al. 10.1109/TGRS.2022.3162582
55. Active Learning Based 3D Semantic Labeling From Images and Videos M. Rong et al. 10.1109/TCSVT.2021.3079991
56. Cascaded Contextual Reasoning for Large-Scale Point Cloud Semantic Segmentation F. Zhang & X. Xia 10.1109/ACCESS.2023.3248963
57. Enhanced Local Feature Learning With Simple Offset Attention for Semantic Segmentation of Large-Scale Point Clouds D. Chen et al. 10.1109/TGRS.2024.3453966
58. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network H. Luo et al. 10.1109/LGRS.2022.3208589
59. Collapsed Building Detection Using 3D Point Clouds and Deep Learning H. Xiu et al. 10.3390/rs12244057
60. A new point cloud simplification method with feature and integrity preservation by partition strategy S. Wang et al. 10.1016/j.measurement.2022.111173
61. Object-based building instance segmentation from airborne LiDAR point clouds W. Yang et al. 10.1080/01431161.2022.2145582
62. A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning Z. Ballouch et al. 10.3390/rs14143415
63. Local Transformer Network on 3D Point Cloud Semantic Segmentation Z. Wang et al. 10.3390/info13040198
64. Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling Q. Hu et al. 10.1109/TPAMI.2021.3083288
65. 3-D Line Matching Network Based on Matching Existence Guidance and Knowledge Distillation J. Tang et al. 10.1109/JIOT.2024.3429352
66. Inferring Point Cloud Quality via Graph Similarity Q. Yang et al. 10.1109/TPAMI.2020.3047083
67. A Graph Aggregation Convolution and Attention Mechanism Based Semantic Segmentation Method for Sparse Lidar Point Cloud Data T. Zheng et al. 10.1109/ACCESS.2023.3339657
68. Continuous Mapping Convolution for Large-Scale Point Clouds Semantic Segmentation K. Yan et al. 10.1109/LGRS.2021.3107006
69. The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review J. Zhao et al. 10.3390/rs13204029
70. Weakly supervised point cloud semantic segmentation based on scene consistency Y. Niu et al. 10.1007/s10489-024-05822-2
71. Paying attention for adjacent areas: Learning discriminative features for large-scale 3D scene segmentation M. Li et al. 10.1016/j.patcog.2022.108722
72. Boundary-Aware and Semiautomatic Segmentation of 3-D Object in Point Clouds H. Luo et al. 10.1109/LGRS.2020.2987747
73. RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration Y. Wang et al. 10.3390/rs17020345
74. Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game D. Zai et al. 10.1016/j.isprsjprs.2017.10.001
75. Semantic-guided 3D building reconstruction from triangle meshes S. Wang et al. 10.1016/j.jag.2023.103324
76. Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global R. Huang et al. 10.1016/j.isprsjprs.2020.02.020
77. GLSNet++: Global and Local-Stream Feature Fusion for LiDAR Point Cloud Semantic Segmentation Using GNN Demixing Block R. Bao et al. 10.1109/JSEN.2023.3345747
78. New Technology on 3D Point Cloud by Laser Measurement of Various Platforms T. SATOH et al. 10.2493/jjspe.85.223
79. A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation S. González-Collazo et al. 10.1016/j.eswa.2023.120672
80. Prospects and Challenges with Legal Informatics and Legal Metrology Framework in the Context of Industry 6.0 S. Chourasia et al. 10.1007/s12647-023-00664-8
81. Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey R. Zhang et al. 10.3390/electronics12173642
82. Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method S. Yang et al. 10.1080/17538947.2024.2385081
83. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al. 10.1007/s40725-024-00228-2
84. An Improved Explainable Point Cloud Classifier (XPCC) N. Arnold et al. 10.1109/TAI.2022.3150647
85. DCNet: Large-Scale Point Cloud Semantic Segmentation With Discriminative and Efficient Feature Aggregation F. Yin et al. 10.1109/TCSVT.2023.3239541
86. Methodology of Detection and Classification of Selected Aviation Obstacles Based on UAV Dense Image Matching M. Lalak & D. Wierzbicki 10.1109/JSTARS.2022.3149105
87. Characterization and Analysis of Deep Learning for 3D Point Cloud Analytics B. Hyun et al. 10.1109/LCA.2021.3099117
88. OpenMPD: An Open Multimodal Perception Dataset for Autonomous Driving X. Zhang et al. 10.1109/TVT.2022.3143173
89. 3D Lidar Point Cloud Segmentation for Automated Driving R. Abbasi et al. 10.1109/MITS.2023.3325854
90. A Comprehensive Review of Vision-Based 3D Reconstruction Methods L. Zhou et al. 10.3390/s24072314
91. Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles Y. Xu et al. 10.1109/JSTARS.2018.2817227
92. Status, advancements and prospects of deep learning methods applied in forest studies T. Yun et al. 10.1016/j.jag.2024.103938
93. A network-level sidewalk inventory method using mobile LiDAR and deep learning Q. Hou & C. Ai 10.1016/j.trc.2020.102772
94. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds F. Matrone & M. Martini 10.4995/var.2021.15318
95. CRBeDaSet: A Benchmark Dataset for High Accuracy Close Range 3D Object Reconstruction G. Gabara & P. Sawicki 10.3390/rs15041116
96. Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey N. Qin et al. 10.1016/j.jag.2023.103566
97. Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B Y. Lian et al. 10.1109/JSTARS.2020.3035274
98. Semantic Segmentation for Point Clouds via Semantic-Based Local Aggregation and Multi-Scale Global Pyramid S. Cao et al. 10.3390/machines11010011
99. Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net Y. Wang & K. Koo 10.3390/app12010395
100. Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation Y. Xie et al. 10.1109/MGRS.2019.2937630
101. BushNet: Effective semantic segmentation of bush in large-scale point clouds H. Wei et al. 10.1016/j.compag.2021.106653
102. Learning local contextual features for 3D point clouds semantic segmentation by attentive kernel convolution G. Tong et al. 10.1007/s00371-023-02819-9
103. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM) Y. Cai et al. 10.3390/rs13071367
104. Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Z. Guo & C. Feng 10.1080/13658816.2018.1552790
105. Machine Learning Generalisation across Different 3D Architectural Heritage E. Grilli & F. Remondino 10.3390/ijgi9060379
106. A Local-Global Feature Fusing Method for Point Clouds Semantic Segmentation Y. Bi et al. 10.1109/ACCESS.2023.3293161
107. Spatial Feature-Based Prioritization for Transmission of Point Cloud Data in 3D-Image Sensor Networks M. Oka et al. 10.1109/JSEN.2021.3106963
108. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds R. Huang et al. 10.1016/j.isprsjprs.2021.04.017
109. 3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data M. Letard et al. 10.1016/j.isprsjprs.2023.11.022
110. Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing Z. Ding et al. 10.3390/robotics12040100
111. Determination of clay-water contact angle via molecular dynamics and deep-learning enhanced methods X. Song & Z. Zhang 10.1007/s11440-021-01238-1
112. MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification Y. Li et al. 10.3390/rs14225707
113. LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas Z. Ye et al. 10.3390/ijgi9070450
114. SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds Q. Hu et al. 10.1007/s11263-021-01554-9
115. A review on artificial intelligence applications for facades A. Duran et al. 10.1016/j.buildenv.2024.112310
116. A review of three dimensional reconstruction techniques J. Phang et al. 10.1007/s11042-021-10605-9
117. Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods K. Turgut et al. 10.1186/s13007-022-00857-3
118. Deep learning based computer vision under the prism of 3D point clouds: a systematic review K. Tychola et al. 10.1007/s00371-023-03237-7
119. A training dataset for semantic segmentation of urban point cloud map for intelligent vehicles H. Song et al. 10.1016/j.isprsjprs.2022.02.007
120. TPDNet: A point cloud data denoising method for offshore drilling platforms and its application C. Ran et al. 10.1016/j.measurement.2024.115671
121. Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies A. Klimkowska et al. 10.3390/rs14112579
122. DPC-Net: Distributed Point Convolution Network for large-scale point clouds semantic segmentation Y. Shao et al. 10.12688/cobot.17468.1
123. SyS3DS: Systematic Sampling of Large-Scale LiDAR Point Clouds for Semantic Segmentation in Forestry Robotics H. Mukhandi et al. 10.3390/s24030823
124. MS-RRFSegNet: Multiscale Regional Relation Feature Segmentation Network for Semantic Segmentation of Urban Scene Point Clouds H. Luo et al. 10.1109/TGRS.2020.2985695
125. Building contour extraction from hypervoxel growth point cloud surface neighborhood azimuth geometric features X. Chen et al. 10.1016/j.jobe.2025.111914
126. Truncated octree and its applications N. Koh et al. 10.1007/s00371-021-02130-5
127. A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds R. Zhang et al. 10.1016/j.jag.2021.102411
128. Deep Learning Based Point Cloud Processing Techniques A. Hazer & R. Yildirim 10.1109/ACCESS.2022.3226211
129. A Simple Semantic-Based Data Storage Layout for Querying Point Clouds S. El-Mahgary et al. 10.3390/ijgi9020072
130. An efficient global energy optimization approach for robust 3D plane segmentation of point clouds Z. Dong et al. 10.1016/j.isprsjprs.2018.01.013
131. Object detection for automotive radar point clouds – a comparison N. Scheiner et al. 10.1186/s42467-021-00012-z
132. Region-Based Edge Convolutions With Geometric Attributes for the Semantic Segmentation of Large-Scale 3-D Point Clouds J. Contreras et al. 10.1109/JSTARS.2020.2998037
133. DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar N. Singer & V. Asari 10.1109/ACCESS.2021.3094127
134. RAAFNet: Reverse Attention Adaptive Fusion Network for Large-Scale Point Cloud Semantic Segmentation K. Wang & H. Zhang 10.3390/math12162485
135. 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 10.3390/rs16234466
136. Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications S. Lytkin et al. 10.3390/rs15112735
137. Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds Z. Xu et al. 10.3390/buildings12070968
138. Spatial Adaptive Fusion Consistency Contrastive Constraint: Weakly Supervised Building Facade Point Cloud Semantic Segmentation Y. Su et al. 10.1109/TGRS.2023.3297631
139. A local tangent plane distance-based approach to 3D point cloud segmentation via clustering H. Chen et al. 10.1016/j.patcog.2023.109307
140. A Deep Neural Network Using Double Self-Attention Mechanism for ALS Point Cloud Segmentation L. Yu et al. 10.1109/ACCESS.2022.3158438
141. A Novel Deep Learning-Based Approach for Rift and Iceberg Recognition From ICESat-2 Data Z. Huang et al. 10.1109/TGRS.2024.3382573
142. Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors L. Zhang et al. 10.1109/TGRS.2021.3091380
143. Exploiting 2D/3D Geomatics Data for the Management, Promotion, and Valorization of Underground Built Heritage L. Gorgoglione et al. 10.3390/smartcities6010012
144. FG-Net: A Fast and Accurate Framework for Large-Scale LiDAR Point Cloud Understanding K. Liu et al. 10.1109/TCYB.2022.3159815
145. A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation K. Zhang et al. 10.1109/TGRS.2022.3201902
146. Efficient multi-modal high-precision semantic segmentation from MLS point cloud without 3D annotation Y. Wang et al. 10.1016/j.jag.2024.104243
147. Pyramid Point: A Multilevel Focusing Network for Revisiting Feature Layers N. Varney & V. Asari 10.1109/LGRS.2022.3191743
148. Learning-based robotic grasping: A review Z. Xie et al. 10.3389/frobt.2023.1038658
149. STSD:A large-scale benchmark for semantic segmentation of subway tunnel point cloud H. Cui et al. 10.1016/j.tust.2024.105829
150. Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information H. Fang & F. Lafarge 10.1016/j.isprsjprs.2019.06.010
151. A large-scale point cloud semantic segmentation neural network based on long-range contextual dependencies enhancement H. Zhang et al. 10.1080/2150704X.2024.2343131
152. Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Z. Ballouch et al. 10.3390/rs16020329
153. Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks A. Rizaldy et al. 10.3390/rs10111723
154. BSTS: A Weakly-Supervised Method for Semantic Learning of 3D Point Clouds Y. Liu et al. 10.1109/TCSVT.2024.3420150
155. Large-Scale Point Cloud Contour Extraction via 3-D-Guided Multiconditional Residual Generative Adversarial Network Y. Zhang et al. 10.1109/LGRS.2019.2917319
156. Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds V. Kasireddy & B. Akinci 10.1061/JCCEE5.CPENG-5041
157. The Mixed Kernel Function SVM-Based Point Cloud Classification C. Chen et al. 10.1007/s12541-019-00102-3
158. BubblEX: An Explainable Deep Learning Framework for Point-Cloud Classification F. Matrone et al. 10.1109/JSTARS.2022.3195200
159. Classification and Change Detection in Mobile Mapping LiDAR Point Clouds M. Voelsen et al. 10.1007/s41064-021-00148-x
160. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage R. Pierdicca et al. 10.3390/rs12061005
161. Urban vegetation segmentation using terrestrial LiDAR point clouds based on point non-local means network Y. Chen et al. 10.1016/j.jag.2021.102580
162. Out-of-core outlier removal for large-scale indoor point clouds L. Ge & J. Feng 10.1016/j.gmod.2022.101142
163. Weld seam object detection system based on the fusion of 2D images and 3D point clouds using interpretable neural networks S. Wang et al. 10.1038/s41598-024-71989-w
164. Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor D. Chen et al. 10.3390/rs13163146
165. A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment B. Xiang et al. 10.1109/TGRS.2019.2916625
166. Object recognition datasets and challenges: A review A. Salari et al. 10.1016/j.neucom.2022.01.022
167. A Multiscale Convolutional Neural Network With Color Vegetation Indices for Semantic Labeling of Point Cloud H. Zhang et al. 10.1109/LGRS.2021.3079242
168. Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview E. Camuffo et al. 10.3390/s22041357
169. An Improved RandLa-Net Algorithm Incorporated with NDT for Automatic Classification and Extraction of Raw Point Cloud Data Z. Ma et al. 10.3390/electronics11172795
170. A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++ K. Chang et al. 10.3390/agriculture14050720
171. Evaluating the Quality of Semantic Segmented 3D Point Clouds E. Barnefske & H. Sternberg 10.3390/rs14030446
172. RdmkNet & Toronto-RDMK: Large-Scale Datasets for Road Marking Classification and Segmentation J. Du et al. 10.1109/TITS.2024.3394481
173. Deep Learning for LiDAR Point Cloud Classification in Remote Sensing A. Diab et al. 10.3390/s22207868
174. A Graph-Voxel Joint Convolution Neural Network for ALS Point Cloud Segmentation J. Zhang et al. 10.1109/ACCESS.2020.3013293
175. Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review Y. Li et al. 10.1109/TNNLS.2020.3015992
176. WHU-Urban3D: An urban scene LiDAR point cloud dataset for semantic instance segmentation X. Han et al. 10.1016/j.isprsjprs.2024.02.007
177. The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo M. Kölle et al. 10.1016/j.ophoto.2021.100001
178. An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation M. Huang et al. 10.3390/rs11232727
179. 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. 10.3390/rs12111875
180. A Robust Automatic Method to Extract Building Facade Maps from 3D Point Cloud Data B. Yu et al. 10.3390/rs14163848
181. Deep Learning-Based Damage Detection from Aerial SfM Point Clouds M. Mohammadi et al. 10.3390/drones3030068
182. Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method A. Dutta et al. 10.1109/TPAMI.2018.2869744
183. Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment Y. Liao et al. 10.3390/drones4020024
184. Boundary–Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation L. He et al. 10.3390/app13064053
185. Fast and Lite Point Cloud Semantic Segmentation for Autonomous Driving Utilizing LiDAR Synthetic Training Data J. Jeong et al. 10.1109/ACCESS.2022.3184803
186. Building facade structure extraction method based on three-dimensional laser point cloud by considering semantic information X. Hu et al. 10.1080/19475683.2024.2335953
187. VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments K. Saleh et al. 10.1109/TITS.2023.3271414
188. Semi-automated Cleaning of Laser Scanning Campaigns with Machine Learning P. Marais et al. 10.1145/3292027
189. Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model Y. Xu et al. 10.1109/JSTARS.2019.2951293
190. Material augmented semantic segmentation of point clouds for building elements H. Liang et al. 10.1111/mice.13198
191. A Review of General Methods for Quantifying and Estimating Urban Trees and Biomass M. Yang et al. 10.3390/f13040616
192. Efficient 3D Scene Semantic Segmentation via Active Learning on Rendered 2D Images M. Rong et al. 10.1109/TIP.2023.3286708
193. 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. 10.3390/rs15143480
194. Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis D. Schunck et al. 10.1371/journal.pone.0256340
195. A New Open-Source Software to Help Design Models for Automatic 3D Point Cloud Classification in Coastal Studies X. Pellerin Le Bas et al. 10.3390/rs16162891
196. An Efficient Hypergraph Approach to Robust Point Cloud Resampling Q. Deng et al. 10.1109/TIP.2022.3149225
197. A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds Z. Sha et al. 10.1109/LGRS.2020.3037484
198. Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving R. Abbasi et al. 10.1109/TITS.2022.3167957
199. Discrimination of Plant Structures in 3D Point Cloud Through Back-Projection of Labels Derived from 2D Semantic Segmentation T. Imabuchi & K. Kawabata 10.20965/jrm.2024.p0063
200. Intensity-Assisted ICP for Fast Registration of 2D-LIDAR Y. Tian et al. 10.3390/s19092124
201. 3-D Object Classification in Heterogeneous Point Clouds via Bag-of-Words and Joint Distribution Adaption H. Luo et al. 10.1109/LGRS.2019.2911200
202. Explicit Incorporation of Spatial Autocorrelation in 3D Deep Learning for Geospatial Object Detection T. Chen et al. 10.1080/24694452.2024.2380898
203. AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation W. Jing et al. 10.3390/rs14041036
204. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation F. Matrone et al. 10.3390/ijgi9090535
205. Juggling with representations: On the information transfer between imagery, point clouds, and meshes for multi-modal semantics D. Laupheimer & N. Haala 10.1016/j.isprsjprs.2021.03.007
206. HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation C. Wen et al. 10.1007/s10462-025-11111-2
207. A survey on end‐to‐end point cloud learning X. Tang et al. 10.1049/ipr2.12729
208. WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation B. Qiu et al. 10.1109/TITS.2024.3469546
209. Instance Segmentation of Industrial Point Cloud Data E. Agapaki & I. Brilakis 10.1061/(ASCE)CP.1943-5487.0000972
210. Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots T. Miao et al. 10.1093/gigascience/giab031
211. MSIDA-Net: Point Cloud Semantic Segmentation via Multi-Spatial Information and Dual Adaptive Blocks F. Shuang et al. 10.3390/rs14092187
212. Optimizing rural waste management: Leveraging high-resolution remote sensing and GIS for efficient collection and routing X. Cheng et al. 10.1016/j.jag.2024.104219
213. Evaluating Deep Learning Advances for Point Cloud Semantic Segmentation in Urban Environments H. Yan et al. 10.1007/s42489-025-00185-1
214. PU-FPG: Point cloud upsampling via form preserving graph convolutional networks H. Wang et al. 10.3233/JIFS-232490
215. DCARN: Deep Context Aware Recurrent Neural Network for Semantic Segmentation of Large Scale Unstructured 3D Point Cloud S. Mehmood et al. 10.1007/s11063-020-10368-8
216. Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods B. Gao et al. 10.1109/TITS.2021.3076844
217. Automatic voxel-based 3D indoor reconstruction and room partitioning from triangle meshes P. Hübner et al. 10.1016/j.isprsjprs.2021.07.002
218. Designing and Building an Intelligent Pavement Management System for Urban Road Networks M. Moradi & G. Assaf 10.3390/su15021157
219. Generation of Thermal Point Clouds From Uncalibrated Thermal Infrared Image Sequences and Mobile Laser Scans J. Zhu et al. 10.1109/TIM.2023.3284942
220. A GPU-Accelerated Framework for Simulating LiDAR Scanning A. Lopez et al. 10.1109/TGRS.2022.3165746
221. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation S. Sarker et al. 10.1007/s00138-024-01543-1
222. QuickFPS: Architecture and Algorithm Co-Design for Farthest Point Sampling in Large-Scale Point Clouds M. Han et al. 10.1109/TCAD.2023.3274922
223. 3D point cloud descriptors: state-of-the-art X. Han et al. 10.1007/s10462-023-10486-4
224. Robust point cloud normal estimation via multi-level critical point aggregation J. Zhou et al. 10.1007/s00371-024-03532-x
225. An Automatic Method for Tree Species Point Cloud Segmentation Based on Deep Learning L. Pu et al. 10.1007/s12524-021-01358-x
226. Color guided convolutional network for point cloud semantic segmentation J. Yang et al. 10.1177/17298806221098506
227. Context-Aware Network for Semantic Segmentation Toward Large-Scale Point Clouds in Urban Environments C. Liu et al. 10.1109/TGRS.2022.3182776
228. SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints P. Testolina et al. 10.1109/TITS.2023.3257086
229. Point cloud features suitable for automatic labeling of MMS point cloud data G. TAKAHASHI & H. MASUDA 10.4287/jsprs.60.266
230. Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues N. Qin et al. 10.1016/j.isprsjprs.2023.06.005
231. Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds H. Lei et al. 10.1109/TPAMI.2020.2983410
232. 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. 10.1080/19475705.2024.2327399
233. Diffusion and inpainting of reflectance and height LiDAR orthoimages P. Biasutti et al. 10.1016/j.cviu.2018.10.011
234. RESEARCH FOR JUDGING ROAD BLOCKAGE CAUSED BY COLLAPSED BLOCK WALLS USING POINT CLOUD DATA Y. UMEHARA et al. 10.2208/jscejj.22-22019
235. Towards a Meaningful 3D Map Using a 3D Lidar and a Camera J. Jeong et al. 10.3390/s18082571
236. Data Preparation Impact on Semantic Segmentation of 3D Mobile LiDAR Point Clouds Using Deep Neural Networks R. Mahmoudi Kouhi et al. 10.3390/rs15040982
237. Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++ A. Esmoris et al. 10.1109/ACCESS.2022.3211072
238. Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset J. Behley et al. 10.1177/02783649211006735
239. Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping J. Deschaud et al. 10.3390/rs13224713
240. Vo-Norvana : Versatile Framework for Efficient Segmentation of Large Point Cloud Data Sets E. Che & M. Olsen 10.1061/JCCEE5.CPENG-4979
241. A Survey of Label-Efficient Deep Learning for 3D Point Clouds A. Xiao et al. 10.1109/TPAMI.2024.3416302
242. Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition N. Qin et al. 10.1016/j.isprsjprs.2018.03.011
243. A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation Z. Yang et al. 10.3390/s24227222
244. Hierarchical Weight Averaging for Deep Neural Networks X. Gu et al. 10.1109/TNNLS.2023.3255540
245. Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor M. Ibrahim et al. 10.3390/rs13183621
246. A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data W. Li et al. 10.1109/TGRS.2021.3076107
247. UrbanLF: A Comprehensive Light Field Dataset for Semantic Segmentation of Urban Scenes H. Sheng et al. 10.1109/TCSVT.2022.3187664
248. MFFRand: Semantic Segmentation of Point Clouds Based on Multi-Scale Feature Fusion and Multi-Loss Supervision Z. Miao et al. 10.3390/electronics11213626
249. Revue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile S. Daniel 10.1139/geomat-2018-0001
250. A multi-scale fully convolutional network for semantic labeling of 3D point clouds M. Yousefhussien et al. 10.1016/j.isprsjprs.2018.03.018
251. A multi-granularity semisupervised active learning for point cloud semantic segmentation S. Ye et al. 10.1007/s00521-023-08455-7
252. Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data X. Wei et al. 10.1109/TITS.2024.3461735
253. An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios E. Özdemir et al. 10.3390/rs13101985
254. Building Extraction from Terrestrial Laser Scanning Data with Density of Projected Points on Polar Grid and Adaptive Threshold M. Chen et al. 10.3390/rs13214392
255. A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds Q. Chen et al. 10.1016/j.jag.2022.102974
256. One Class One Click: Quasi scene-level weakly supervised point cloud semantic segmentation with active learning P. Wang et al. 10.1016/j.isprsjprs.2023.09.002
257. Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis Y. Zhang et al. 10.3390/rs13061136
258. PReFormer: A memory-efficient transformer for point cloud semantic segmentation P. Akwensi et al. 10.1016/j.jag.2024.103730
259. Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network H. Hu et al. 10.1080/17538947.2024.2310083
260. MuSic-UDF: Learning Multi-Scale dynamic grid representation for high-fidelity surface reconstruction from point clouds C. Jin et al. 10.1016/j.cag.2024.104081
261. Deep learning-based fast prediction of flow field around multiple bluff bodies P. Lin et al. 10.1016/j.awe.2024.100020
262. Semantic Context Encoding for Accurate 3D Point Cloud Segmentation H. Liu et al. 10.1109/TMM.2020.3007331
263. An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation M. Chen et al. 10.3390/rs16224240
264. FFA-Net: fast feature aggregation network for 3D point cloud segmentation R. Cheng et al. 10.1007/s00138-023-01434-x
265. Large-scale point cloud contour extraction via 3D guided multi-conditional generative adversarial network W. Zhang et al. 10.1016/j.isprsjprs.2020.04.003
266. ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation J. Du et al. 10.1016/j.isprsjprs.2021.09.024
267. Optimal LiDAR Data Resolution Analysis for Object Classification M. Darrah et al. 10.3390/s22145152
268. MVF-CNN: Fusion of Multilevel Features for Large-Scale Point Cloud Classification Y. Li et al. 10.1109/ACCESS.2019.2908983
269. 3D Semantic Scene Completion: A Survey L. Roldão et al. 10.1007/s11263-021-01504-5
270. Lidar Mapping Optimization Based on Lightweight Semantic Segmentation Z. Zhao et al. 10.1109/TIV.2019.2919432
271. 基于最小平方中值的点云平面拟合算法 王. Wang Yang et al. 10.3788/LOP213208
272. Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks A. Godo et al. 10.1109/ACCESS.2022.3156108
273. Mesh Convolution With Continuous Filters for 3-D Surface Parsing H. Lei et al. 10.1109/TNNLS.2023.3281871
274. Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey L. Rauch & T. Braml 10.3390/app13169146
275. IGN: Instance-Guided Net for 3-D Instance Segmentation in Cluttered Scenes via Monocular Depth Sensor Z. Wan et al. 10.1109/JSEN.2024.3416149
276. Graph Neural Networks in Point Clouds: A Survey D. Li et al. 10.3390/rs16142518
277. SVLA: A compact supervoxel segmentation method based on local allocation H. Ni & X. Niu 10.1016/j.isprsjprs.2020.03.011
278. Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators T. Shinohara et al. 10.1109/JSTARS.2021.3124610
279. Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review S. Yang et al. 10.3390/rs15030548
280. Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration A. Zaganidis et al. 10.1109/LRA.2018.2848308
281. 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. 10.3788/CJL231411
282. Deep Learning-Based Semantic Segmentation and Surface Reconstruction for Point Clouds of Offshore Oil Production Equipment Z. Wang et al. 10.1109/TGRS.2023.3348797
283. PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification G. Zhao et al. 10.3390/rs13214312
284. NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer X. Hu et al. 10.3390/s22145128
285. Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation M. Ibrahim et al. 10.1109/ACCESS.2021.3062547
286. 3D Point Cloud Dataset of Heavy Construction Equipment S. Park & S. Kim 10.3390/app14093599
287. Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet E. Barnefske & H. Sternberg 10.1109/ACCESS.2022.3233411
288. Positive-Negative Receptive Field Reasoning for Omni-Supervised 3D Segmentation X. Tan et al. 10.1109/TPAMI.2023.3319470
289. PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large-Scale Application J. Li et al. 10.1155/2022/9433661
290. Backward Attentive Fusing Network With Local Aggregation Classifier for 3D Point Cloud Semantic Segmentation H. Shuai et al. 10.1109/TIP.2021.3073660
291. Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations Y. Cai et al. 10.1109/TGRS.2022.3161982
292. Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines H. Yu et al. 10.3390/rs15092371
293. CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation G. Tong et al. 10.1109/ACCESS.2020.2992612
294. Neighborhood co-occurrence modeling in 3D point cloud segmentation J. Gong et al. 10.1007/s41095-021-0244-6
295. Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification I. Farmakis et al. 10.3390/rs13071354
296. Deep Learning on 3D Semantic Segmentation: A Detailed Review T. Betsas et al. 10.3390/rs17020298
297. A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models X. Yao et al. 10.1007/s10845-022-01939-8
298. 3D CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data B. Nagy & C. Benedek 10.1109/JSEN.2019.2927269
299. Distributed Optimization Based on Graph Filter for Ensuring Invariant Simplification of High-Volume Point Cloud X. Zhou et al. 10.1109/TCE.2023.3272773
300. Using Relative Projection Density for Classification of Terrestrial Laser Scanning Data with Unknown Angular Resolution M. Chen et al. 10.3390/rs14236043
301. Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows S. Xia & R. Wang 10.1109/JSTARS.2019.2897987
302. A Survey of Synthetic Data Augmentation Methods in Machine Vision A. Mumuni et al. 10.1007/s11633-022-1411-7
303. Hausdorff point convolution with geometric priors L. Lin et al. 10.1007/s11432-021-3311-2
304. Deep Learning for Generating Time-of-Flight Camera Artifacts T. Müller et al. 10.3390/jimaging10100246
305. A Review on Deep Learning Techniques for 3D Sensed Data Classification D. Griffiths & J. Boehm 10.3390/rs11121499
306. SegTrans: Semantic Segmentation With Transfer Learning for MLS Point Clouds S. Shen et al. 10.1109/LGRS.2023.3294748
307. Designing and Testing a UAV Mapping System for Agricultural Field Surveying M. Christiansen et al. 10.3390/s17122703
308. 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. 10.1109/TGRS.2023.3339475
309. Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation X. Geng et al. 10.3390/rs13040691
310. Weakly supervised point cloud segmentation via deep morphological semantic information embedding W. Xue et al. 10.1049/cit2.12239
311. GL-Net: Semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation J. Li et al. 10.1016/j.isprsjprs.2023.04.011
312. Rethinking Design and Evaluation of 3D Point Cloud Segmentation Models T. Zoumpekas et al. 10.3390/rs14236049
313. Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds Y. Xiao et al. 10.3390/rs12030473
314. Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations M. Sánchez et al. 10.3390/s22155599
315. Shield Tunnel Dislocation Detection Method Based on Semantic Segmentation and Bolt Hole Positioning of MLS Point Cloud H. Cui et al. 10.1109/TGRS.2024.3386308
316. Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning K. Shang et al. 10.1016/j.jmsy.2023.10.017
317. An Accurate and Efficient Supervoxel Re-Segmentation Approach for Large-Scale Point Clouds Using Plane Constraints B. Lai et al. 10.3390/rs15163973
318. KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D Y. Liao et al. 10.1109/TPAMI.2022.3179507
319. Context-Aware 3D Point Cloud Semantic Segmentation With Plane Guidance T. Weng et al. 10.1109/TMM.2022.3212914
320. Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds H. Luo et al. 10.1016/j.jag.2021.102564
321. RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging K. Liu 10.1007/s11263-022-01740-3
322. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation F. Matrone et al. 10.1080/17538947.2024.2390457
323. 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 10.3390/f14112169
324. Deep Learning for 3D Point Clouds: A Survey Y. Guo et al. 10.1109/TPAMI.2020.3005434
325. Review: Deep Learning on 3D Point Clouds S. Bello et al. 10.3390/rs12111729
326. Multilevel Ground Segmentation for 3-D Point Clouds of Outdoor Scenes Based on Shape Analysis Y. An et al. 10.1109/TIM.2022.3154835
327. A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation J. Han et al. 10.1109/TIP.2024.3372446
328. Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework Z. Zeng et al. 10.1016/j.jag.2024.104309
329. Semantic segmentation of large-scale point clouds with neighborhood uncertainty Y. Bao et al. 10.1007/s11042-023-17814-4
330. Towards Urban Scene Semantic Segmentation with Deep Learning from LiDAR Point Clouds: A Case Study in Baden-Württemberg, Germany Y. Zou et al. 10.3390/rs13163220
331. Nested Architecture Search for Point Cloud Semantic Segmentation F. Yang et al. 10.1109/TIP.2022.3147983
332. RESEARCH ON CREATION OF IDENTIFICATION MODELS OF ROAD FEATURES USING HD MAPS R. IMAI et al. 10.2208/jscejj.22-22022