Lin, C.H.[Chao-Hung],
Chen, J.Y.[Jyun-Yuan],
Su, P.L.[Po-Lin],
Chen, C.H.[Chung-Hao],
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Elsevier DOI
1407
Point cloud classification.
linear, planar, or spherical.
BibRef
Wang, Z.[Zhen],
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Li, R.J.[Rou-Jing],
Zheng, Y.B.[Yi-Bo],
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GeoRS(56), No. 8, August 2018, pp. 4594-4604.
IEEE DOI
1808
Large number of overlapping objects.
feature extraction, geophysical image processing,
image classification, image representation, spatial pooling
BibRef
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Indahl, U.G.[Ulf Geir],
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XCRF,
PandRS(155), 2019, pp. 90-101.
Elsevier DOI
1908
Point cloud classification, Overfitting problem, Conditional random field
BibRef
Tong, G.F.[Guo-Feng],
Li, Y.[Yong],
Zhang, W.L.[Wei-Long],
Chen, D.[Dong],
Zhang, Z.X.[Zhen-Xin],
Yang, J.C.[Jing-Chao],
Zhang, J.J.[Jian-Jun],
Point Set Multi-Level Aggregation Feature Extraction Based on
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DOI Link
1912
BibRef
Ng, Y.T.[Yong Thiang],
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Tian, J.[Jing],
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SIViP(14), No. 4, June 2020, pp. 747-752.
Springer DOI
2005
BibRef
Tong, G.F.[Guo-Feng],
Li, Y.[Yong],
Chen, D.[Dong],
Xia, S.B.[Shao-Bo],
Peethambaran, J.[Jiju],
Wang, Y.B.[Yue-Bin],
Multi-View Features Joint Learning with Label and Local Distribution
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RS(12), No. 1, 2020, pp. xx-yy.
DOI Link
2001
Noise from outdoor sensors.
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Wen, C.C.[Cong-Cong],
Yang, L.[Lina],
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Peng, L.[Ling],
Chi, T.[Tianhe],
Directionally constrained fully convolutional neural network for
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PandRS(162), 2020, pp. 50-62.
Elsevier DOI
2004
Airborne LiDAR, Point cloud classification,
Directionlly constrained nearest neighbor, ISPRS 3D labeling
BibRef
Zhang, X. .L.[Xin- Liang],
Fu, C.L.[Chen-Lin],
Zhao, Y.J.[Yun-Ji],
Xu, X.Z.[Xiao-Zhuo],
Hybrid feature CNN model for point cloud classification and
segmentation,
IET-IPR(14), No. 16, 19 December 2020, pp. 4086-4091.
DOI Link
2103
BibRef
Wen, C.C.[Cong-Cong],
Li, X.[Xiang],
Yao, X.J.[Xiao-Jing],
Peng, L.[Ling],
Chi, T.[Tianhe],
Airborne LiDAR point cloud classification with global-local graph
attention convolution neural network,
PandRS(173), 2021, pp. 181-194.
Elsevier DOI
2102
Airborne LiDAR, Point cloud classification,
Point cloud deep learning, Graph attention convolution, ISPRS 3D labeling
BibRef
Chen, Y.[Yang],
Liu, G.L.[Guan-Lan],
Xu, Y.M.[Ya-Ming],
Pan, P.[Pai],
Xing, Y.[Yin],
PointNet++ Network Architecture with Individual Point Level and
Global Features on Centroid for ALS Point Cloud Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Guo, R.[Rui],
Zhou, Y.[Yong],
Zhao, J.Q.[Jia-Qi],
Man, Y.Y.[Yi-Yun],
Liu, M.J.[Min-Jie],
Yao, R.[Rui],
Liu, B.[Bing],
Point cloud classification by dynamic graph CNN with adaptive feature
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IET-CV(15), No. 3, 2021, pp. 235-244.
DOI Link
2106
BibRef
Gu, R.B.[Rui-Bin],
Wu, Q.X.[Qiu-Xia],
Ng, W.W.Y.[Wing W.Y.],
Xu, H.B.[Hong-Bin],
Wang, Z.Y.[Zhi-Yong],
ERINet: Enhanced Rotation-Invariant Network for Point Cloud
Classification,
PRL(151), 2021, pp. 180-186.
Elsevier DOI
2110
Point cloud classification, Rotation invariance, 3D Deep learning
BibRef
Qiu, S.[Shi],
Anwar, S.[Saeed],
Barnes, N.[Nick],
Geometric Back-Projection Network for Point Cloud Classification,
MultMed(24), No. 2022, pp. 1943-1955.
IEEE DOI
2204
BibRef
Earlier:
Dense-Resolution Network for Point Cloud Classification and
Segmentation,
WACV21(3812-3821)
IEEE DOI
2106
Feature extraction, Task analysis, Geometry, Visualization, Shape,
Redundancy, Point Cloud Classification, 3D Deep Learning,
Error-correcting Feedback.
Training, Visualization, Adaptation models, Computational modeling
BibRef
Gu, R.B.[Rui-Bin],
Wu, Q.X.[Qiu-Xia],
Li, Y.Q.[Yu-Qiong],
Kang, W.X.[Wen-Xiong],
Ng, W.W.Y.[Wing W. Y.],
Wang, Z.Y.[Zhi-Yong],
Enhanced Local and Global Learning for Rotation-Invariant Point Cloud
Representation,
MultMedMag(29), No. 4, October 2022, pp. 24-37.
IEEE DOI
2301
Point cloud compression, Representation learning,
Supervised learning, Perturbation methods, Unsupervised learning, Task analysis
BibRef
Dang, J.S.[Ji-Sheng],
Yang, J.[Jun],
LHPHGCNN: Lightweight Hierarchical Parallel Heterogeneous Group
Convolutional Neural Networks for Point Cloud Scene Prediction,
ITS(23), No. 10, October 2022, pp. 18903-18915.
IEEE DOI
2210
BibRef
Earlier:
HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for
Point Clouds Processing,
ACCV20(I:20-37).
Springer DOI
2103
Convolution, Point cloud compression, Encoding, Semantics, Shape,
Feature extraction, 3D point cloud classification/segmentation,
lightweight hierarchical parallel heterogeneous
group convolutional neural network
BibRef
Li, X.[Xiang],
Wen, C.C.[Cong-Cong],
Cao, Q.M.[Qi-Ming],
Du, Y.L.[Yan-Lei],
Fang, Y.[Yi],
Retraction: A novel semi-supervised method for airborne LiDAR point cloud
classification,
PandRS(188), 2022, pp. 141.
Elsevier DOI
2205
BibRef
And: Original reference
PandRS(180), 2021, pp. 117-129.
Elsevier DOI
2109
Airborne LiDAR, Point cloud classification,
Semi-supervised classification, Siamese self-supervision
BibRef
Zhang, C.J.[Chun-Jiao],
Xu, S.H.[Sheng-Hua],
Jiang, T.[Tao],
Liu, J.P.[Ji-Ping],
Liu, Z.J.[Zheng-Jun],
Luo, A.[An],
Ma, Y.[Yu],
Integrating Normal Vector Features into an Atrous Convolution
Residual Network for LiDAR Point Cloud Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Wang, W.M.[Wei-Ming],
You, Y.[Yang],
Liu, W.[Wenhai],
Lu, C.[Cewu],
Point cloud classification with deep normalized Reeb graph
convolution,
IVC(106), 2021, pp. 104092.
Elsevier DOI
2102
Reeb graph, Point cloud, Graph normalization
BibRef
You, Y.[Yang],
Ye, Z.L.[Ze-Lin],
Lou, Y.J.[Yu-Jing],
Li, C.K.[Cheng-Kun],
Li, Y.L.[Yong-Lu],
Ma, L.Z.[Li-Zhuang],
Wang, W.M.[Wei-Ming],
Lu, C.[Cewu],
Canonical Voting: Towards Robust Oriented Bounding Box Detection in
3D Scenes,
CVPR22(1183-1192)
IEEE DOI
2210
Point cloud compression, Deep learning, Machine vision,
Object detection, Sensor systems and applications,
Vision applications and systems
BibRef
Mao, Y.Q.[Yong-Qiang],
Chen, K.Q.[Kai-Qiang],
Diao, W.H.[Wen-Hui],
Sun, X.[Xian],
Lu, X.N.[Xiao-Nan],
Fu, K.[Kun],
Weinmann, M.[Martin],
Beyond single receptive field: A receptive field
fusion-and-stratification network for airborne laser scanning point
cloud classification,
PandRS(188), 2022, pp. 45-61.
Elsevier DOI
2205
Airborne laser scanning, Point cloud, Classification,
Deep learning, Dilated graph convolution, Multi-scale receptive fields
BibRef
Xu, Z.L.[Ze-Lin],
Liu, K.J.[Kang-Jun],
Chen, K.[Ke],
Ding, C.X.[Chang-Xing],
Wang, Y.W.[Yao-Wei],
Jia, K.[Kui],
Classification of single-view object point clouds,
PR(135), 2023, pp. 109137.
Elsevier DOI
2212
Point cloud classification, Rotation equivariance, Pose estimation
BibRef
Zhu, L.[Lei],
Chen, W.N.[Wei-Nan],
Lin, X.[Xubin],
He, L.[Li],
Guan, Y.S.[Yi-Sheng],
Curvature-Variation-Inspired Sampling for Point Cloud Classification
and Segmentation,
SPLetters(29), 2022, pp. 1868-1872.
IEEE DOI
2209
Point cloud compression, Shape, Task analysis, Geometry,
Sampling methods, Convolution, Curvature variation,
point cloud
BibRef
He, Y.Q.[Yun-Qian],
Zhang, Z.[Zhi],
Wang, Z.[Zhe],
Luo, Y.K.[Yong-Kang],
Su, L.[Li],
Li, W.[Wanyi],
Wang, P.[Peng],
Zhang, W.[Wen],
IPC-Net: Incomplete point cloud classification network based on data
augmentation and similarity measurement,
JVCIR(91), 2023, pp. 103769.
Elsevier DOI
2303
Incomplete point clouds, Point cloud classification,
Data augmentation, Similarity measurement
BibRef
Ye, C.G.[Chuang-Guan],
Zhu, H.Y.[Hong-Yuan],
Zhang, B.[Bo],
Chen, T.[Tao],
A Closer Look at Few-Shot 3D Point Cloud Classification,
IJCV(131), No. 3, March 2023, pp. 772-795.
Springer DOI
2302
BibRef
Zhao, Z.[Zhi],
Ma, Y.X.[Yan-Xin],
Xu, K.[Ke],
Wan, J.W.[Jian-Wei],
Deep Hybrid Compression Network for Lidar Point Cloud Classification
and Segmentation,
RS(15), No. 16, 2023, pp. 4015.
DOI Link
2309
BibRef
Yu, Y.G.[You-Guang],
Zhang, W.[Wei],
Yang, F.Z.[Fu-Zheng],
Li, G.[Ge],
Rate-Distortion Optimized Geometry Compression for Spinning LiDAR
Point Cloud,
MultMed(25), 2023, pp. 2993-3005.
IEEE DOI
2309
BibRef
Paul, S.[Sneha],
Patterson, Z.[Zachary],
Bouguila, N.[Nizar],
Improved Training for 3D Point Cloud Classification,
SSSPR22(253-263).
Springer DOI
2301
WWW Link.
BibRef
Shi, X.[Xian],
Xu, X.[Xun],
Zhang, W.[Wanyue],
Zhu, X.T.[Xia-Tian],
Foo, C.S.[Chuan Sheng],
Jia, K.[Kui],
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding,
ICPR22(5045-5051)
IEEE DOI
2212
Point cloud compression, Training, Solid modeling,
Semantics, Semisupervised learning, Stability analysis
BibRef
Wang, R.B.[Rui-Bin],
Yang, Y.[Yibo],
Tao, D.C.[Da-Cheng],
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers
via Adversarial Rotation,
CVPR22(14351-14360)
IEEE DOI
2210
Point cloud compression, Training, Deep learning,
Computational modeling, Training data, Robustness, Representation learning
BibRef
Zhou, M.,
Kang, Z.,
Wang, Z.,
Kong, M.,
Airborne Lidar Point Cloud Classification Fusion with Dim Point Cloud,
ISPRS20(B2:375-382).
DOI Link
2012
BibRef
Farella, E.M.,
Torresani, A.,
Remondino, F.,
Sparse Point Cloud Filtering Based On Covariance Features,
CIPA19(465-472).
DOI Link
1912
BibRef
Özdemir, E.,
Remondino, F.,
Golkar, A.,
Aerial Point Cloud Classification With Deep Learning and Machine
Learning Algorithms,
SMPR19(843-849).
DOI Link
1912
BibRef
Özdemir, E.,
Remondino, F.,
Classification of Aerial Point Clouds With Deep Learning,
Semantics3D19(103-110).
DOI Link
1912
BibRef
Grilli, E.,
Poux, F.,
Remondino, F.,
Unsupervised Object-based Clustering in Support of Supervised
Point-based 3d Point Cloud Classification,
ISPRS21(B2-2021: 471-478).
DOI Link
2201
BibRef
Grilli, E.,
Menna, F.,
Remondino, F.,
A Review of Point Clouds Segmentation And Classification Algorithms,
3DARCH17(339-344).
DOI Link
1805
BibRef
Karami, A.,
Menna, F.,
Remondino, F.,
Investigating 3d Reconstruction of Non-collaborative Surfaces Through
Photogrammetry and Photometric Stereo,
ISPRS21(B2-2021: 519-526).
DOI Link
2201
BibRef
Uy, M.A.,
Pham, Q.,
Hua, B.,
Nguyen, T.,
Yeung, S.,
Revisiting Point Cloud Classification: A New Benchmark Dataset and
Classification Model on Real-World Data,
ICCV19(1588-1597)
IEEE DOI
2004
Dataset, Point Cloud.
WWW Link. CAD, feature extraction,
learning (artificial intelligence), neural nets, Market research
BibRef
Roveri, R.[Riccardo],
Rahmann, L.[Lukas],
Öztireli, A.C.[A. Cengiz],
Gross, M.[Markus],
A Network Architecture for Point Cloud Classification via Automatic
Depth Images Generation,
CVPR18(4176-4184)
IEEE DOI
1812
Network architecture, Neural networks, Task analysis
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Range Data, Point Cloud Processing and Analysis .