11.2.4.8.2 Large-Scale 3-D Semantic Object Detection

Zhao, J.Q.[Jun-Qiao], Zhu, Q.[Qing], Du, Z.Q.[Zhi-Qiang], Feng, T.T.[Tian-Tian], Zhang, Y.T.[Ye-Ting],
Mathematical morphology-based generalization of complex 3D building models incorporating semantic relationships,
PandRS(68), No. 1, March 2012, pp. 95-111.
Elsevier DOI 1204
Generalization; Complex 3D building; Mathematical morphology; Levels of Detail; Semantic relationships BibRef

Zhu, Q.[Qing], Zhao, J.Q.[Jun-Qiao], Du, Z.Q.[Zhi-Qiang], Zhang, Y.T.[Ye-Ting], Xu, W.P.[Wei-Ping], Xie, X.[Xiao], Ding, Y.L.[Yu-Lin], Wang, F.[Fei], Wang, T.S.[Ting-Song],
Towards Semantic 3D City Modeling and Visual Explorations,
GeoInfo10(xx-yy).
PDF File. 1011
BibRef

Zhang, L.Q.[Li-Qiang], Zhang, L.[Liang],
Deep Learning-Based Classification and Reconstruction of Residential Scenes From Large-Scale Point Clouds,
GeoRS(56), No. 4, April 2018, pp. 1887-1897.
IEEE DOI 1804
Buildings, Feature extraction, Image reconstruction, Semantics, Shape, Solid modeling, Classification, reconstruction BibRef

Zhang, L.Q.[Li-Qiang], Li, Z.Q.[Zhu-Qiang], Li, A.J.[An-Jian], Liu, F.Y.[Fang-Yu],
Large-scale urban point cloud labeling and reconstruction,
PandRS(138), 2018, pp. 86-100.
Elsevier DOI 1804
ReLu-NN, Point cloud parsing, Building reconstruction BibRef

Huang, R.[Rong], Xu, Y.S.[Yu-Sheng], Hong, D.F.[Dan-Feng], Yao, W.[Wei], Ghamisi, P.[Pedram], Stilla, U.[Uwe],
Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global,
PandRS(163), 2020, pp. 62-81.
Elsevier DOI 2005
Semantic labeling, ALS point cloud, Deep learning, Feature embedding, Manifold learning, Graph optimization BibRef

Zhu, J.W.[Jing-Wei], Gehrung, J.[Joachim], Huang, R.[Rong], Borgmann, B.[Björn], Sun, Z.H.[Zheng-Hao], Hoegner, L.[Ludwig], Hebel, M.[Marcus], Xu, Y.S.[Yu-Sheng], Stilla, U.[Uwe],
TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Zheng, X.W.[Xian-Wei], Huan, L.X.[Lin-Xi], Xia, G.S.[Gui-Song], Gong, J.Y.[Jian-Ya],
Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss,
PandRS(170), 2020, pp. 15-28.
Elsevier DOI 2011
Semantic segmentation, Convolutional neural network (ConvNet), Edge-aware loss BibRef

Neupane, B.[Bipul], Horanont, T.[Teerayut], Aryal, J.[Jagannath],
Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Cai, Y.Z.[Yuan-Zhi], Huang, H.[Hong], Wang, K.Y.[Kai-Yang], Zhang, C.[Cheng], Fan, L.[Lei], Guo, F.Y.[Fang-Yu],
Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM),
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wang, L.[Libo], Li, R.[Rui], Wang, D.Z.[Dong-Zhi], Duan, C.X.[Chen-Xi], Wang, T.[Teng], Meng, X.L.[Xiao-Liang],
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Du, J.[Jing], Cai, G.R.[Guo-Rong], Wang, Z.Y.[Zong-Yue], Huang, S.F.[Shang-Feng], Su, J.H.[Jin-He], Marcato Junior, J.[José], Smit, J.[Julian], Li, J.[Jonathan],
ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation,
PandRS(182), 2021, pp. 37-51.
Elsevier DOI 2112
Large-scale point clouds, Semantic segmentation, Joint residual-dense optimization, Deep learning BibRef

Gao, Q.[Qian], Shen, X.[Xukun],
ThickSeg: Efficient semantic segmentation of large-scale 3D point clouds using multi-layer projection,
IVC(108), 2021, pp. 104161.
Elsevier DOI 2104
3D point cloud, Semantic segmentation, Convolutional neural network, Large scale BibRef

Xiao, A.[Aoran], Yang, X.F.[Xiao-Fei], Lu, S.J.[Shi-Jian], Guan, D.[Dayan], Huang, J.X.[Jia-Xing],
FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation,
PandRS(176), 2021, pp. 237-249.
Elsevier DOI 2106
LiDAR, Point cloud, Semantic segmentation, Spherical projection, Autonomous driving, Scene understanding BibRef

Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Xie, L.H.[Lin-Hai], Rosa, S.[Stefano], Guo, Y.L.[Yu-Lan], Wang, Z.H.[Zhi-Hua], Trigoni, N.[Niki], Markham, A.[Andrew],
Learning Semantic Segmentation of Large-Scale Point Clouds With Random Sampling,
PAMI(44), No. 11, November 2022, pp. 8338-8354.
IEEE DOI 2210
BibRef
Earlier:
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds,
CVPR20(11105-11114)
IEEE DOI 2008
Semantics, Memory management, Task analysis, Sampling methods, Space exploration, Feature extraction, Large-scale point clouds, local feature aggregation. Semantics, Feature extraction, Encoding, Benchmark testing BibRef

Liu, H.[Hao], Guo, Y.L.[Yu-Lan], Ma, Y.N.[Yan-Ni], Lei, Y.J.[Yin-Jie], Wen, G.J.[Gong-Jian],
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation,
MultMed(23), 2021, pp. 2045-2055.
IEEE DOI 2107
Semantics, Image segmentation, Encoding, Convolution, semantic context BibRef

Ma, Y.N.[Yan-Ni], Guo, Y.L.[Yu-Lan], Liu, H.[Hao], Lei, Y.J.[Yin-Jie], Wen, G.J.[Gong-Jian],
Global Context Reasoning for Semantic Segmentation of 3D Point Clouds,
WACV20(2920-2929)
IEEE DOI 2006
Semantics, Cognition, Convolution, Feature extraction, Task analysis BibRef

Ma, Y.X.[Yan-Xin], Guo, Y.L.[Yu-Lan], Lei, Y.J.[Yin-Jie], Lu, M.[Min], Zhang, J.[Jun],
3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation,
ICPR18(1560-1566)
IEEE DOI 1812
Feature extraction, Semantics, Labeling, Neural networks, Task analysis BibRef

Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Fang, G.C.[Guang-Chi], Guo, Y.L.[Yu-Lan], Leonardis, A.[Aleš], Trigoni, N.[Niki], Markham, A.[Andrew],
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds,
ECCV22(XXVII:600-619).
Springer DOI 2211
BibRef

Rim, B.[Beanbonyka], Lee, A.[Ahyoung], Hong, M.[Min],
Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder-Decoder Shared MLPs with Multiple Losses,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, M.[Min], Kadam, P.[Pranav], Liu, S.[Shan], Kuo, C.C.J.[C.C. Jay],
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds,
PRL(164), 2022, pp. 9-15.
Elsevier DOI 2212
Point cloud, Semantic segmentation, Indoor scene understanding, Green learning, unsupervised learning BibRef

Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Khalid, S.[Sheikh], Xiao, W.[Wen], Trigoni, N.[Niki], Markham, A.[Andrew],
SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds,
IJCV(130), No. 2, February 2022, pp. 316-343.
Springer DOI 2202

WWW Link. Dataset, Urban. Urban scale point cloud BibRef

Yin, F.[Fukun], Huang, Z.L.[Zi-Long], Chen, T.[Tao], Luo, G.Z.[Guo-Zhong], Yu, G.[Gang], Fu, B.[Bin],
DCNet: Large-Scale Point Cloud Semantic Segmentation With Discriminative and Efficient Feature Aggregation,
CirSysVideo(33), No. 8, August 2023, pp. 4083-4095.
IEEE DOI 2308
Point cloud compression, Semantics, Semantic segmentation, Decoding, Aggregates, Feature extraction, Semantic segmentation, attention BibRef

Gao, F.J.[Feng-Jiao], Yan, Y.M.[Yi-Ming], Lin, H.[Hemin], Shi, R.Y.[Rui-Yao],
PIIE-DSA-Net for 3D Semantic Segmentation of Urban Indoor and Outdoor Datasets,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yu, M.[Mulin], Lafarge, F.[Florent], Oesau, S.[Sven], Hilaire, B.[Bruno],
Repairing geometric errors in 3D urban models with kinetic data structures,
PandRS(192), 2022, pp. 315-326.
Elsevier DOI 2209
Mesh repairing, Semantic reconstruction, Kinetic data structure, BIM, CityGML, IFC BibRef

Sambaturu, B.[Bhavani], Gupta, A.[Ashutosh], Jawahar, C.V., Arora, C.[Chetan],
ScribbleNet: Efficient interactive annotation of urban city scenes for semantic segmentation,
PR(133), 2023, pp. 109011.
Elsevier DOI 2210
BibRef

Tang, Q.[Quan], Liu, F.G.[Fa-Gui], Zhang, T.[Tong], Jiang, J.[Jun], Zhang, Y.[Yu], Zhu, B.[Boyuan], Tang, X.[Xuhao],
Compensating for Local Ambiguity With Encoder-Decoder in Urban Scene Segmentation,
ITS(23), No. 10, October 2022, pp. 19224-19235.
IEEE DOI 2210
Image segmentation, Semantics, Logic gates, Transformers, Computational modeling, Convolutional neural networks, autonomous driving BibRef

Wu, L.[Letian], Zhang, X.[Xian], Zhu, D.J.[De-Jun], Yang, W.K.[Wan-Kou],
BFANet: Effective segmentation network for low altitude high-resolution urban scene image,
JVCIR(94), 2023, pp. 103847.
Elsevier DOI 2306
Urban scene segmentation, Real-time semantic segmentation, Bi-lateral network, Convolutional neural network BibRef

Zhang, G.Y.[Guang-Yun], Zhang, R.T.[Rong-Ting],
MeshNet-SP: A Semantic Urban 3D Mesh Segmentation Network with Sparse Prior,
RS(15), No. 22, 2023, pp. 5324.
DOI Link 2311
BibRef

Wang, J.X.[Jing-Xue], Li, H.[Huan], Xu, Z.H.[Zheng-Hui], Xie, X.[Xiao],
Semantic Segmentation of Urban Airborne LiDAR Point Clouds Based on Fusion Attention Mechanism and Multi-Scale Features,
RS(15), No. 21, 2023, pp. 5248.
DOI Link 2311
BibRef

Ji, H.[Hao], Yang, S.[Sansheng], Jiang, Z.P.[Zhi-Peng], Zhang, J.J.[Jian-Jun], Guo, S.[Shuhao], Li, G.[Gaorui], Zhong, S.[Saishang], Liu, Z.[Zheng], Xie, Z.[Zhong],
BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion,
RS(15), No. 22, 2023, pp. 5342.
DOI Link 2311
BibRef

Han, J.W.[Jia-Wei], Liu, K.Q.[Kai-Qi], Li, W.[Wei], Chen, G.Z.[Guang-Zhi], Wang, W.G.[Wen-Guang], Zhang, F.[Feng],
A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation,
IP(33), 2024, pp. 2004-2017.
IEEE DOI 2403
Point cloud compression, Semantic segmentation, Task analysis, Knowledge engineering, Transformers, Image coding, information combination BibRef

Wang, J.Y.[Jing-Yi], Liu, Y.[Yu], Tan, H.L.[Han-Lin], Zhang, M.J.[Mao-Jun],
A Survey on Weakly Supervised 3D Point Cloud Semantic Segmentation,
IET-CV(18), No. 3, 2024, pp. 329-342.
DOI Link 2404
learning (artificial intelligence), unsupervised learning BibRef

Wang, J.Y.[Jing-Yi], He, J.Y.[Jing-Yang], Liu, Y.[Yu], Chen, C.[Chen], Zhang, M.J.[Mao-Jun], Tan, H.L.[Han-Lin],
Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation,
RS(16), No. 17, 2024, pp. 3319.
DOI Link 2409
BibRef

Yuan, T.B.[Tie-Biao], Yu, Y.Y.[Yang-Yang], Wang, X.L.[Xiao-Long],
Semantic segmentation of large-scale point clouds by integrating attention mechanisms and transformer models,
IVC(146), 2024, pp. 105019.
Elsevier DOI 2405
Point cloud semantic segmentation, Large-scale point cloud, Transformer, Slot attention, Loss function BibRef

Zhang, J.J.[Jian-Jun], Jiang, Z.P.[Zhi-Peng], Qiu, Q.J.[Qin-Jun], Liu, Z.[Zheng],
TCFAP-Net: Transformer-based Cross-feature Fusion and Adaptive Perception Network for large-scale point cloud semantic segmentation,
PR(154), 2024, pp. 110630.
Elsevier DOI Code:
WWW Link. 2406
Transformer, Attention, Semantic segmentation, Point cloud scenarios BibRef

Li, M.T.[Meng-Tian], Lin, S.H.[Shao-Hui], Wang, Z.H.[Zi-Han], Shen, Y.H.[Yun-Hang], Zhang, B.C.[Bao-Chang], Ma, L.Z.[Li-Zhuang],
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization,
PR(156), 2024, pp. 110701.
Elsevier DOI 2408
3D point cloud, Class-imbalanced learning, Semi-supervised learning, Semantic segmentation BibRef

Zhou, C.[Ce], Shu, Z.[Zhaokun], Shi, L.[Li], Ling, Q.[Qiang],
Semantic segmentation for large-scale point clouds based on hybrid attention and dynamic fusion,
PR(156), 2024, pp. 110798.
Elsevier DOI 2408
Hybrid attention, Dynamic fusion, Point cloud, Semantic segmentation BibRef

Zhao, H.Y.[Hao-Ying], Zhou, A.[Aimin],
DPANet: Position-aware feature encoding and decoding for accurate large-scale point cloud semantic segmentation,
IET-CV(18), No. 8, 2024, pp. 1376-1389.
DOI Link 2501
image segmentation, pattern recognition BibRef

Gao, L.[Lin], Liu, Y.[Yu], Chen, X.[Xi], Liu, Y.X.[Yu-Xiang], Yan, S.[Shen], Zhang, M.[Maojun],
CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset,
RS(16), No. 6, 2024, pp. 1079.
DOI Link 2403
BibRef

Wang, Y.F.[Yue-Feng], Jiao, W.[Wei], Fan, H.C.[Hong-Chao], Zhou, G.Q.[Guo-Qing],
A framework for fully automated reconstruction of semantic building model at urban-scale using textured LoD2 data,
PandRS(216), 2024, pp. 90-108.
Elsevier DOI Code:
WWW Link. 2408
Semantic building model, CityGML LoD3, Facade layout graph, Parameter representation, EM-MAP optimization BibRef

Liu, P.[Peng], Ge, Y.Q.[Yan-Qi], Duan, L.X.[Li-Xin], Li, W.[Wen], Luo, H.[Haonan], Lv, F.[Fengmao],
Transferring Multi-Modal Domain Knowledge to Uni-Modal Domain for Urban Scene Segmentation,
ITS(25), No. 9, September 2024, pp. 11576-11589.
IEEE DOI 2409
Training, Semantic segmentation, Transformers, Task analysis, Adaptation models, Visualization, Synthetic data, multi-modal learning BibRef

Schreiber, Q.[Qendrim], Wolpert, N.[Nicola], Schömer, E.[Elmar],
METNet: A mesh exploring approach for segmenting 3D textured urban scenes,
PandRS(218), 2024, pp. 498-509.
Elsevier DOI Code:
WWW Link. 2412
Texture meshes, Semantic segmentation, Urban scene, Large scene understanding, Structured representation, Artificial neural networks BibRef

Jia, X.[Xinqi], Song, X.Y.[Xiao-Yong], Rao, L.[Lei], Fan, G.Y.[Guang-Yu], Cheng, S.L.[Song-Lin], Chen, N.S.[Nian-Sheng],
DEUFormer: High-Precision Semantic Segmentation for Urban Remote Sensing Images,
IET-CV(18), No. 8, 2024, pp. 1209-1222.
DOI Link 2501
convolutional neural nets BibRef

Sun, T.F.[Tian-Fang], Zhang, Z.Z.[Zhi-Zhong], Tan, X.[Xin], Qu, Y.Y.[Yan-Yun], Xie, Y.[Yuan],
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning,
IP(33), 2024, pp. 1838-1852.
IEEE DOI 2403
Point cloud compression, Labeling, Laser radar, Annotations, Training, Semantic segmentation, point cloud semantic segmentation BibRef

Xu, X.F.[Xiang-Feng], Zhang, P.[Pinyi], Huang, W.X.[Wen-Xuan], Shen, Y.H.[Yun-Hang], Chen, H.S.[Hao-Sheng], Lin, J.Z.[Jing-Zhong], Li, W.[Wei], He, G.Q.[Gao-Qi], Xie, J.[Jiao], Lin, S.H.[Shao-Hui],
Weakly Supervised Semantic Segmentation via Progressive Confidence Region Expansion,
CVPR25(9829-9838)
IEEE DOI Code:
WWW Link. 2508
Weak supervision, Costs, Codes, Annotations, Semantic segmentation, Prototypes, Benchmark testing, Transformers, weakly supervised semantic segmentation BibRef

Li, M.T.[Meng-Tian], Xie, Y.[Yuan], Shen, Y.H.[Yun-Hang], Ke, B.[Bo], Qiao, R.Z.[Rui-Zhi], Ren, B.[Bo], Lin, S.H.[Shao-Hui], Ma, L.Z.[Li-Zhuang],
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization,
CVPR22(14910-14919)
IEEE DOI 2210
Point cloud compression, Training, Costs, Shape, Computational modeling, Computer vision for social good, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Zhang, Y.C.[Ya-Chao], Qu, Y.Y.[Yan-Yun], Xie, Y.[Yuan], Li, Z.H.[Zong-Hao], Zheng, S.S.[Shan-Shan], Li, C.H.[Cui-Hua],
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation,
ICCV21(15500-15508)
IEEE DOI 2203
Point cloud compression, Correlation, Network topology, Annotations, Semantics, Supervised learning, Vision for robotics and autonomous vehicles BibRef

Zi, W.J.[Wen-Jie], Chen, H.[Hao], Li, J.[Jun], Wu, J.J.[Jiang-Jiang],
MambaMeshSeg-Net: A Large-Scale Urban Mesh Semantic Segmentation Method Using a State Space Model with a Hybrid Scanning Strategy,
RS(17), No. 9, 2025, pp. 1653.
DOI Link 2505
BibRef

Xiao, Y.[Yu], Wu, H.[Hui], Chen, Y.S.[Yi-Sheng], Chen, C.C.[Chong-Cheng], Dong, R.[Ruihai], Lin, D.[Ding],
Hybrid Offset Position Encoding for Large-Scale Point Cloud Semantic Segmentation,
RS(17), No. 2, 2025, pp. 256.
DOI Link 2502
BibRef

Zhou, Y.[Yuan], Sun, Q.[Qi], Meng, J.[Jin], Hu, Q.L.[Qing-Long], Lyu, J.H.[Jia-Hang], Wang, Z.W.[Zhi-Wei], Wang, S.F.[Shi-Feng],
PointCartesian-Net: enhancing 3D coordinates for semantic segmentation of large-scale point clouds,
JOSA-A(38), No. 8, August 2021, pp. 1194-1200.
DOI Link 2503
Machine learning, Machine vision, Neural networks, Point clouds, Segmentation, Virtual reality BibRef

Wang, H.Y.[Hao-Yu], Wang, F.S.[Fa-Sheng], Wang, M.Y.[Meng-Yin], Sun, F.[Fuming], Li, H.J.[Hao-Jie],
Rethinking How to Capture Long-Range Dependency in 3D Object Detection,
CirSysVideo(35), No. 7, July 2025, pp. 6671-6683.
IEEE DOI Code:
WWW Link. 2507
Feature extraction, Detectors, Object detection, Computer architecture, Computational efficiency, Training, dense feature map BibRef

Wu, X.Y.[Xiao-Yang], Tian, Z.[Zhuotao], Wen, X.[Xin], Peng, B.[Bohao], Liu, X.H.[Xi-Hui], Yu, K.C.[Kai-Cheng], Zhao, H.S.[Heng-Shuang],
Towards Large-Scale 3D Representation Learning with Multi-Dataset Point Prompt Training,
CVPR24(19551-19562)
IEEE DOI 2410
Training, Representation learning, Deep learning, Point cloud compression, Solid modeling, Soft sensors, 3D Semantic Segmentation BibRef

Yoo, S.[Sunghwan], Jeong, Y.[Yeonjeong], Jameela, M.[Maryam], Sohn, G.[Gunho],
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes,
PCV23(6577-6586)
IEEE DOI 2309
BibRef

Liu, B.[Bing], Wu, H.Z.[Hui-Zhu], Bao, X.L.[Xue-Liang], Zhong, Z.H.[Zhao-Hao],
LPCUNet: A Lightweight Pure CNN UNet for Efficient Urban Scene Remote Sensing Semantic Segmentation,
CVIDL23(57-61)
IEEE DOI 2403
Fuses, Semantic segmentation, Biological system modeling, Transformers, Feature extraction, Decoding, semantic segmentation BibRef

Das, A.[Anurag], Xian, Y.Q.[Yong-Qin], He, Y.[Yang], Akata, Z.[Zeynep], Schiele, B.[Bernt],
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation,
WACV23(5967-5976)
IEEE DOI 2302
Training, Costs, Annotations, Semantic segmentation, Semantics, Data models, segmentation BibRef

Ren, T.Q.[Tian-Qi], Shen, Q.[Qiu], Fu, Y.[Ying], You, S.D.[Shao-Di],
Point-Supervised Semantic Segmentation of Natural Scenes via Hyperspectral Imaging,
PBDL24(1357-1367)
IEEE DOI 2410
Training, Degradation, Costs, Accuracy, Annotations, Semantic segmentation BibRef

Huang, Y.X.[Yu-Xing], Shen, Q.[Qiu], Fu, Y.[Ying], You, S.D.[Shao-Di],
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image,
PBDL21(1117-1126)
IEEE DOI 2112
Image segmentation, Visualization, Costs, Annotations, Semantics, Urban areas, Manuals BibRef

Robert, D.[Damien], Vallet, B.[Bruno], Landrieu, L.[Loic],
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation,
CVPR22(5565-5574)
IEEE DOI 2210
Point cloud compression, Image sensors, Image segmentation, Solid modeling, Image analysis, Semantics, Scene analysis and understanding BibRef

Zhou, Y.S.[Yun-Song], Zhu, H.Z.[Hong-Zi], Li, C.Q.[Chun-Qin], Cui, T.K.[Tian-Kai], Chang, S.[Shan], Guo, M.[Minyi],
TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series,
ICCV21(7098-7107)
IEEE DOI 2203
Point cloud compression, Computational modeling, Semantics, Time series analysis, Feature extraction, Propagation losses, BibRef

Illarionova, S.[Svetlana], Nesteruk, S.[Sergey], Shadrin, D.[Dmitrii], Ignatiev, V.[Vladimir], Pukalchik, M.[Mariia], Oseledets, I.[Ivan],
Object-Based Augmentation for Building Semantic Segmentation: Ventura and Santa Rosa Case Study,
ILDAV21(1659-1668)
IEEE DOI 2112
Training, Image segmentation, Satellites, Semantics, Buildings, Pipelines BibRef

Akadas, K.[Kiran], Gangisetty, S.[Shankar],
3d Semantic Segmentation for Large-scale Scene Understanding,
MLCSA20(87-102).
Springer DOI 2103
BibRef

Robert, D.[Damien], Raguet, H.[Hugo], Landrieu, L.[Loic],
Efficient 3D Semantic Segmentation with Superpoint Transformer,
ICCV23(17149-17158)
IEEE DOI 2401
BibRef

Landrieu, L.[Loic], Simonovsky, M.,
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs,
CVPR18(4558-4567)
IEEE DOI 1812
Shape, Semantics, Image segmentation, Image edge detection, Pipelines BibRef

Liu, F., Li, S., Zhang, L., Zhou, C., Ye, R., Wang, Y., Lu, J.,
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-Scale 3D Point Clouds,
ICCV17(5679-5688)
IEEE DOI 1802
convolution, feature extraction, grammars, image classification, image segmentation, BibRef

Martinovic, A.[Andelo], Knopp, J.[Jan], Riemenschneider, H.[Hayko], Van Gool, L.J.[Luc J.],
3D all the way: Semantic segmentation of urban scenes from start to end in 3D,
CVPR15(4456-4465)
IEEE DOI 1510
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Instance Segmentation, Point Cloud Segmentation .