Lin, C.H.[Chao-Hung],
Chen, J.Y.[Jyun-Yuan],
Su, P.L.[Po-Lin],
Chen, C.H.[Chung-Hao],
Eigen-feature analysis of weighted covariance matrices for LiDAR
point cloud classification,
PandRS(94), No. 1, 2014, pp. 70-79.
Elsevier DOI
1407
Point cloud classification.
linear, planar, or spherical.
BibRef
Wang, Z.[Zhen],
Zhang, L.Q.[Li-Qiang],
Zhang, L.[Liang],
Li, R.J.[Rou-Jing],
Zheng, Y.B.[Yi-Bo],
Zhu, Z.D.[Zi-Dong],
A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point
Cloud Classification,
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
Arief, H.A.[Hasan Asy'ari],
Indahl, U.G.[Ulf Geir],
Strand, G.H.[Geir-Harald],
Tveite, H.[Hĺvard],
Addressing overfitting on point cloud classification using Atrous
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
Multi-Scale Max Pooling and LDA for Point Cloud Classification,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Ng, Y.T.[Yong Thiang],
Huang, C.M.[Chung Ming],
Li, Q.T.[Qing Tao],
Tian, J.[Jing],
RadialNet: a point cloud classification approach using local structure
representation with radial basis function,
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
Consistency for Point Cloud Classification,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link
2001
Noise from outdoor sensors.
BibRef
Wen, C.C.[Cong-Cong],
Yang, L.[Lina],
Li, X.[Xiang],
Peng, L.[Ling],
Chi, T.[Tianhe],
Directionally constrained fully convolutional neural network for
airborne LiDAR point cloud classification,
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
fusion,
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.M.[Nick M.],
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.B.[Xu-Bin],
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
Wei, L.[Lei],
Wan, S.[Shuai],
Wang, Z.C.[Zhe-Cheng],
Yang, F.Z.[Fu-Zheng],
Near-Lossless Compression of Point Cloud Attribute Using Quantization
Parameter Cascading and Rate-Distortion Optimization,
MultMed(26), 2024, pp. 3317-3330.
IEEE DOI
2402
Point cloud compression, Quantization (signal), Distortion,
Transforms, Geometry, Encoding, Point cloud compression,
rate distortion optimization
BibRef
Hao, F.[Fengda],
Li, J.J.[Jiao-Jiao],
Song, R.[Rui],
Li, Y.S.[Yun-Song],
Cao, K.[Kailang],
Structure-Aware Graph Convolution Network for Point Cloud Parsing,
MultMed(25), 2023, pp. 7025-7036.
IEEE DOI
2311
BibRef
Qian, Y.[Yue],
Hou, J.H.[Jun-Hui],
Zhang, Q.J.[Qi-Jian],
Zeng, Y.M.[Yi-Ming],
Kwong, S.[Sam],
He, Y.[Ying],
Task-Oriented Compact Representation of 3D Point Clouds via A Matrix
Optimization-Driven Network,
CirSysVideo(33), No. 11, November 2023, pp. 6981-6995.
IEEE DOI
2311
BibRef
Sun, C.[Chao],
Zheng, Z.D.[Zhe-Dong],
Wang, X.H.[Xiao-Han],
Xu, M.L.[Ming-Liang],
Yang, Y.[Yi],
Self-Supervised Point Cloud Representation Learning via Separating
Mixed Shapes,
MultMed(25), 2023, pp. 6207-6218.
IEEE DOI
2311
BibRef
Lu, T.[Tao],
Liu, C.X.[Chun-Xu],
Chen, Y.X.[You-Xin],
Wu, G.S.[Gang-Shan],
Wang, L.M.[Li-Min],
APP-Net: Auxiliary-Point-Based Push and Pull Operations for Efficient
Point Cloud Recognition,
IP(32), 2023, pp. 6500-6513.
IEEE DOI Code:
WWW Link.
2312
BibRef
Huang, R.[Rui],
Pan, X.[Xuran],
Zheng, H.[Henry],
Jiang, H.J.[Hao-Jun],
Xie, Z.F.[Zhi-Feng],
Wu, C.[Cheng],
Song, S.[Shiji],
Huang, G.[Gao],
Joint representation learning for text and 3D point cloud,
PR(147), 2024, pp. 110086.
Elsevier DOI
2312
Point cloud, Multi-modal learning, Representation learning
BibRef
Zhang, Z.[Ziyu],
Da, F.P.[Fei-Peng],
Self-supervised latent feature learning for partial point clouds
recognition,
PRL(176), 2023, pp. 49-55.
Elsevier DOI
2312
Self-supervised learning, Point clouds recognition,
Partial point clouds, Perspective transformation
BibRef
troner, M.[Martin],
Urban, R.[Rudolf],
Línková, L.[Lenka],
Color-Based Point Cloud Classification Using a Novel Gaussian Mixed
Modeling-Based Approach versus a Deep Neural Network,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Letard, M.[Mathilde],
Lague, D.[Dimitri],
Le Guennec, A.[Arthur],
Lefčvre, S.[Sébastien],
Feldmann, B.[Baptiste],
Leroy, P.[Paul],
Girardeau-Montaut, D.[Daniel],
Corpetti, T.[Thomas],
3DMASC: Accessible, explainable 3D point clouds classification.
Application to BI-spectral TOPO-bathymetric lidar data,
PandRS(207), 2024, pp. 175-197.
Elsevier DOI Code:
WWW Link.
2401
Bispectral lidar, Multi-scale classification,
Multi-cloud classification, Feature selection, 3D data, Machine learning
BibRef
Wu, Y.[Yue],
Liu, J.M.[Jia-Ming],
Gong, M.[Maoguo],
Gong, P.R.[Pei-Ran],
Fan, X.L.[Xiao-Long],
Qin, A.K.,
Miao, Q.G.[Qi-Guang],
Ma, W.P.[Wen-Ping],
Self-Supervised Intra-Modal and Cross-Modal Contrastive Learning for
Point Cloud Understanding,
MultMed(26), 2024, pp. 1626-1638.
IEEE DOI
2402
Point cloud compression, Task analysis, Feature extraction,
Self-supervised learning, Image color analysis, Visualization,
point cloud understanding
BibRef
Liu, J.M.[Jia-Ming],
Wu, Y.[Yue],
Gong, M.[Maoguo],
Liu, Z.X.[Zhi-Xiao],
Miao, Q.G.[Qi-Guang],
Ma, W.P.[Wen-Ping],
Inter-Modal Masked Autoencoder for Self-Supervised Learning on Point
Clouds,
MultMed(26), 2024, pp. 3897-3908.
IEEE DOI
2402
Point cloud compression, Transformers, Task analysis, Standards,
Computer architecture, Decoding, Self-supervised learning,
point cloud understanding
BibRef
Zhang, Y.[Yali],
Feng, W.[Wei],
Quan, Y.H.[Ying-Hui],
Ye, G.Q.[Guang-Qiang],
Dauphin, G.[Gabriel],
Dynamic Spatial-Spectral Feature Optimization-Based Point Cloud
Classification,
RS(16), No. 3, 2024, pp. 575.
DOI Link
2402
BibRef
Huang, Q.D.[Qi-Dong],
Dong, X.Y.[Xiao-Yi],
Chen, D.D.[Dong-Dong],
Zhou, H.[Hang],
Zhang, W.M.[Wei-Ming],
Zhang, K.[Kui],
Hua, G.[Gang],
Cheng, Y.Q.[Yue-Qiang],
Yu, N.H.[Neng-Hai],
PointCAT: Contrastive Adversarial Training for Robust Point Cloud
Recognition,
IP(33), 2024, pp. 2183-2196.
IEEE DOI Code:
WWW Link.
2404
Point cloud compression, Training, Robustness, Prototypes,
Perturbation methods, Laser radar, Point cloud recognition, model robustness
BibRef
Feng, T.[Tuo],
Wang, W.G.[Wen-Guan],
Wang, X.H.[Xiao-Han],
Yang, Y.[Yi],
Zheng, Q.H.[Qing-Hua],
Clustering based Point Cloud Representation Learning for 3D Analysis,
ICCV23(8249-8260)
IEEE DOI
2401
BibRef
Yan, S.M.[Si-Ming],
Yang, Z.P.[Zhen-Pei],
Li, H.X.[Hao-Xiang],
Song, C.[Chen],
Guan, L.[Li],
Kang, H.[Hao],
Hua, G.[Gang],
Huang, Q.X.[Qi-Xing],
Implicit Autoencoder for Point-Cloud Self-Supervised Representation
Learning,
ICCV23(14484-14496)
IEEE DOI Code:
WWW Link.
2401
BibRef
Cheng, N.[Nuo],
Li, X.H.[Xiao-Han],
Luo, C.Y.[Chuan-Yu],
Liu, X.T.[Xiao-Tong],
Li, H.[Han],
Lei, S.G.[Sheng-Guang],
Li, P.[Pu],
PSCO: A Point Cloud Scene Classification Model Based on Contrast
Learning,
ICIP23(925-929)
IEEE DOI
2312
BibRef
Lv, H.H.[Huan-Huan],
Jiang, S.R.[Song-Ru],
Sun, Y.M.[Yi-Ming],
Liu, J.[Jia],
Chen, Z.[Zhiyu],
Chen, L.J.[Li-Jun],
MGT-PC: Memory-Guided Transformer For Robust Point Cloud
Classification,
ICIP23(1745-1749)
IEEE DOI
2312
BibRef
Lee, Y.X.[Yu-Xing],
Wu, W.[Wei],
Feature Adversarial Distillation for Point Cloud Classification,
ICIP23(970-974)
IEEE DOI
2312
BibRef
Sun, Y.J.[Ya-Jie],
Zia, A.[Ali],
Zhou, J.[Jun],
Multiscale Representations Learning Transformer Framework for Point
Cloud Classification,
ICIP23(3354-3358)
IEEE DOI
2312
BibRef
Park, G.[Gyudo],
Kang, S.H.[Soo-Hyeok],
Cheng, W.C.[Wen-Can],
Ko, J.H.[Jong Hwan],
Dynamic Inference Acceleration of 3D Point Cloud Deep Neural Networks
Using Point Density and Entropy,
ECV23(4725-4729)
IEEE DOI
2309
BibRef
Lai, X.[Xin],
Chen, Y.[Yukang],
Lu, F.[Fanbin],
Liu, J.H.[Jian-Hui],
Jia, J.Y.[Jia-Ya],
Spherical Transformer for LiDAR-Based 3D Recognition,
CVPR23(17545-17555)
IEEE DOI
2309
BibRef
Park, J.Y.[Jin-Young],
Lee, S.[Sanghyeok],
Kim, S.[Sihyeon],
Xiong, Y.[Yunyang],
Kim, H.W.J.[Hyun-Woo J.],
Self-Positioning Point-Based Transformer for Point Cloud
Understanding,
CVPR23(21814-21823)
IEEE DOI
2309
BibRef
Long, F.[Fuchen],
Yao, T.[Ting],
Qiu, Z.[Zhaofan],
Li, L.[Lusong],
Mei, T.[Tao],
PointClustering: Unsupervised Point Cloud Pre-training using
Transformation Invariance in Clustering,
CVPR23(21824-21834)
IEEE DOI
2309
BibRef
Chen, J.J.[Jia-Jing],
Yang, M.[Minmin],
Velipasalar, S.[Senem],
ViewNet: A Novel Projection-Based Backbone with View Pooling for
Few-shot Point Cloud Classification,
CVPR23(17652-17660)
IEEE DOI
2309
BibRef
Qin, S.W.[Sheng-Wei],
Li, Z.[Zhong],
Liu, L.G.[Li-Gang],
Robust 3D Shape Classification via Non-local Graph Attention Network,
CVPR23(5374-5383)
IEEE DOI
2309
BibRef
Shen, Z.Q.[Zhi-Qiang],
Sheng, X.X.[Xiao-Xiao],
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Liu, Q.[Qiong],
Zhou, X.[Xi],
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on
Point Cloud Videos,
CVPR23(1212-1222)
IEEE DOI
2309
BibRef
Sheng, X.X.[Xiao-Xiao],
Shen, Z.Q.[Zhi-Qiang],
Xiao, G.[Gang],
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Fan, H.[Hehe],
Point Contrastive Prediction with Semantic Clustering for
Self-Supervised Learning on Point Cloud Videos,
ICCV23(16469-16478)
IEEE DOI
2401
BibRef
Shen, Z.Q.[Zhi-Qiang],
Sheng, X.X.[Xiao-Xiao],
Fan, H.[Hehe],
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Liu, Q.[Qiong],
Wen, H.[Hao],
Zhou, X.[Xi],
Masked Spatio-Temporal Structure Prediction for Self-supervised
Learning on Point Cloud Videos,
ICCV23(16534-16543)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liang, H.X.[Han-Xue],
Fan, H.[Hehe],
Fan, Z.W.[Zhi-Wen],
Wang, Y.[Yi],
Chen, T.L.[Tian-Long],
Cheng, Y.[Yu],
Wang, Z.Y.[Zhang-Yang],
Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction,
ECCV22(III:156-172).
Springer DOI
2211
BibRef
Li, X.L.[Xing-Lin],
Chen, J.J.[Jia-Jing],
Ouyang, J.H.[Jin-Hui],
Deng, H.H.[Han-Hui],
Velipasalar, S.[Senem],
Wu, D.[Di],
ToThePoint: Efficient Contrastive Learning of 3D Point Clouds via
Recycling,
CVPR23(21781-21790)
IEEE DOI
2309
BibRef
Zhang, Z.[Zaiwei],
Bai, M.[Min],
Li, L.E.[Li Erran],
Implicit Surface Contrastive Clustering for LiDAR Point Clouds,
CVPR23(21716-21725)
IEEE DOI
2309
BibRef
Chen, Y.J.[Yi-Jun],
Yang, Z.[Zhulun],
Zheng, X.W.[Xian-Wei],
Chang, Y.D.[Ya-Dong],
Li, X.[Xutao],
Pointformer: A Dual Perception Attention-based Network for Point Cloud
Classification,
ACCV22(I:432-449).
Springer DOI
2307
WWW Link.
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 .