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.[Håvard],
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
Ding, X.,
Lin, W.,
Chen, Z.,
Zhang, X.,
Point Cloud Saliency Detection by Local and Global Feature Fusion,
IP(28), No. 11, November 2019, pp. 5379-5393.
IEEE DOI
1909
Saliency detection, Visualization, Videos, saliency
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
Poliyapram, V.[Vinayaraj],
Wang, W.M.[Wei-Min],
Nakamura, R.[Ryosuke],
A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for
Aerial Point Cloud 3D Semantic Segmentation,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Luo, Z.P.[Zhi-Peng],
Liu, D.[Di],
Li, J.[Jonathan],
Chen, Y.P.[Yi-Ping],
Xiao, Z.L.[Zhen-Long],
Junior, J.M.[José Marcato],
Gonçalves, W.N.[Wesley Nunes],
Wang, C.[Cheng],
Learning sequential slice representation with an attention-embedding
network for 3D shape recognition and retrieval in MLS point clouds,
PandRS(161), 2020, pp. 147-163.
Elsevier DOI
2002
MLS point clouds, Sequential slice representation,
Shape recognition, Shape retrieval, Deep learning, Embedding attention strategy
BibRef
Bachhofner, S.[Stefan],
Loghin, A.M.[Ana-Maria],
Otepka, J.[Johannes],
Pfeifer, N.[Norbert],
Hornacek, M.[Michael],
Siposova, A.[Andrea],
Schmidinger, N.[Niklas],
Hornik, K.[Kurt],
Schiller, N.[Nikolaus],
Kähler, O.[Olaf],
Hochreiter, R.[Ronald],
Generalized Sparse Convolutional Neural Networks for Semantic
Segmentation of Point Clouds Derived from Tri-Stereo Satellite
Imagery,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link
2004
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
Xu, S.,
Wang, R.,
Wang, H.,
Zheng, H.,
An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point
Clouds,
ITS(21), No. 7, July 2020, pp. 2765-2776.
IEEE DOI
2007
Laser radar, Clustering algorithms,
Bipartite graph, Roads, Feature extraction, Symmetric matrices,
bipartite graph
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
Shen, Y.M.[Yang-Mei],
Dai, W.[Wenrui],
Li, C.L.[Cheng-Lin],
Zou, J.[Junni],
Xiong, H.K.[Hong-Kai],
Multi-Scale Structured Dictionary Learning for 3-D Point Cloud
Attribute Compression,
CirSysVideo(31), No. 7, July 2021, pp. 2792-2807.
IEEE DOI
2107
Encoding, Geometry, Transforms,
Dictionaries, Machine learning, Sparse matrices, hierarchical sparse coding
BibRef
Chen, C.F.[Chuan-Fa],
Guo, J.J.[Jiao-Jiao],
Wu, H.M.[Hui-Ming],
Li, Y.Y.[Yan-Yan],
Shi, B.[Bo],
Performance Comparison of Filtering Algorithms for High-Density
Airborne LiDAR Point Clouds over Complex LandScapes,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link
2107
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
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
Wang, Y.[Yan],
Zhao, Y.N.[Yi-Ning],
Ying, S.H.[Shi-Hui],
Du, S.Y.[Shao-Yi],
Gao, Y.[Yue],
Rotation-Invariant Point Cloud Representation for 3-D Model
Recognition,
Cyber(52), No. 10, October 2022, pp. 10948-10956.
IEEE DOI
2209
Point cloud compression, Solid modeling, Task analysis,
Convolutional neural networks, Data models, Harmonic analysis,
3-D point cloud
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, L.Y.[Lu-Yang],
He, L.G.[Li-Gang],
Gao, J.J.[Jin-Jin],
Han, X.[Xie],
PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point
Cloud,
CirSysVideo(32), No. 10, October 2022, pp. 6835-6849.
IEEE DOI
2210
Point cloud compression, Data models, Deep learning, Training,
Task analysis, Convolution, Computational modeling, Deep learning, sampling
BibRef
Lu, D.[Dening],
Xie, Q.[Qian],
Gao, K.[Kyle],
Xu, L.L.[Lin-Lin],
Li, J.[Jonathan],
3DCTN: 3D Convolution-Transformer Network for Point Cloud
Classification,
ITS(23), No. 12, December 2022, pp. 24854-24865.
IEEE DOI
2212
Transformers, Point cloud compression, Feature extraction,
Representation learning, Convolutional codes, Costs, Transformer,
graph convolution
BibRef
Qiu, S.[Shi],
Anwar, S.[Saeed],
Barnes, N.[Nick],
PnP-3D: A Plug-and-Play for 3D Point Clouds,
PAMI(45), No. 1, January 2023, pp. 1312-1319.
IEEE DOI
2212
Point cloud compression, Task analysis, Semantics, Visualization,
Deep learning, Pipelines, Point cloud, plug-and-play, 3D deep learning
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
Huang, T.X.[Tian-Xin],
Chen, J.[Jun],
Zhang, J.N.[Jiang-Ning],
Liu, Y.[Yong],
Liang, J.[Jie],
Fast Point Cloud Sampling Network,
PRL(164), 2022, pp. 216-223.
Elsevier DOI
2212
3D Point Cloud, Neural Network, Sampling
BibRef
Huang, T.X.[Tian-Xin],
Zhang, J.N.[Jiang-Ning],
Chen, J.[Jun],
Liu, Y.[Yuang],
Liu, Y.[Yong],
Resolution-Free Point Cloud Sampling Network with Data Distillation,
ECCV22(II:54-70).
Springer DOI
2211
BibRef
Yang, Z.X.[Ze-Xin],
Ye, Q.[Qin],
Stoter, J.[Jantien],
Nan, L.L.[Liang-Liang],
Enriching Point Clouds with Implicit Representations for 3D
Classification and Segmentation,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Zhang, R.[Renrui],
Wang, L.[Liuhui],
Guo, Z.[Ziyu],
Shi, J.B.[Jian-Bo],
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis,
WACV23(1246-1255)
IEEE DOI
2302
Point cloud compression, Knowledge engineering, Deep learning,
Shape, Neural networks, Prototypes, Algorithms: 3D computer vision
BibRef
Yang, M.M.[Min-Min],
Chen, J.J.[Jia-Jing],
Velipasalar, S.[Senem],
Cross-Modality Feature Fusion Network for Few-Shot 3D Point Cloud
Classification,
WACV23(653-662)
IEEE DOI
2302
Point cloud compression, Representation learning, Correlation,
Fuses, Robustness, Algorithms: 3D computer vision
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
Guinard, S.A.[Stephane A.],
Daniel, S.[Sylvie],
Badard, T.[Thierry],
3D point clouds simplification based on geometric primitives and
graph-structured optimization,
ICPR22(3837-3844)
IEEE DOI
2212
Point cloud compression, Geometry, Solid modeling,
Adaptation models, Urban areas, Vegetation
BibRef
Thieshanthan, A.[Arulmolivarman],
Niwarthana, A.[Amashi],
Somarathne, P.[Pamuditha],
Wickremasinghe, T.[Tharindu],
Rodrigo, R.[Ranga],
HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point
Cloud Processing,
ICPR22(2700-2706)
IEEE DOI
2212
Point cloud compression, Representation learning, Laser radar,
Semantic segmentation, Message passing, Feature extraction, Graph neural networks
BibRef
Qiu, Z.F.[Zhao-Fan],
Li, Y.[Yehao],
Wang, Y.[Yu],
Pan, Y.W.[Ying-Wei],
Yao, T.[Ting],
Mei, T.[Tao],
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness
Enhancement,
ECCV22(III:593-609).
Springer DOI
2211
BibRef
Lin, M.[Manxi],
Feragen, A.[Aasa],
DiffConv: Analyzing Irregular Point Clouds with an Irregular View,
ECCV22(III:380-397).
Springer DOI
2211
WWW Link.
BibRef
Chen, W.L.[Wan-Li],
Zhu, X.G.[Xin-Ge],
Chen, G.J.[Guo-Jin],
Yu, B.[Bei],
Efficient Point Cloud Analysis Using Hilbert Curve,
ECCV22(II:730-747).
Springer DOI
2211
BibRef
Potamias, R.A.[Rolandos Alexandros],
Bouritsas, G.[Giorgos],
Zafeiriou, S.P.[Stefanos P.],
Revisiting Point Cloud Simplification:
A Learnable Feature Preserving Approach,
ECCV22(II:586-603).
Springer DOI
2211
BibRef
Cheng, T.Y.[Ta-Ying],
Hu, Q.Y.[Qing-Yong],
Xie, Q.[Qian],
Trigoni, N.[Niki],
Markham, A.[Andrew],
Meta-sampler:
Almost-Universal yet Task-Oriented Sampling for Point Clouds,
ECCV22(II:694-710).
Springer DOI
2211
BibRef
Chen, J.K.[Jun-Kun],
Wang, Y.X.[Yu-Xiong],
PointTree:
Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees,
ECCV22(III:105-120).
Springer DOI
2211
BibRef
Choe, J.[Jaesung],
Park, C.[Chunghyun],
Rameau, F.[Francois],
Park, J.[Jaesik],
Kweon, I.S.[In So],
PointMixer: MLP-Mixer for Point Cloud Understanding,
ECCV22(XXVII:620-640).
Springer DOI
2211
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
Xu, J.Y.[Jian-Yun],
Tang, X.[Xin],
Zhu, Y.[Yushi],
Sun, J.[Jie],
Pu, S.L.[Shi-Liang],
SGMNet: Learning Rotation-Invariant Point Cloud Representations via
Sorted Gram Matrix,
ICCV21(10448-10457)
IEEE DOI
2203
Point cloud compression, Correlation, Shape, Convolution,
Computational modeling, Mathematical models,
3D from multiview and other sensors
BibRef
Ben Izhak, R.[Ran],
Lahav, A.[Alon],
Tal, A.[Ayellet],
AttWalk: Attentive Cross-Walks for Deep Mesh Analysis,
WACV22(2937-2946)
IEEE DOI
2202
3D shape analysis by random walk along mesh to get descriptor.
Deep learning, Shape, Feature extraction, Data mining,
Task analysis, Vision for Graphics 3D Computer Vision
BibRef
Chen, T.[Tian],
Zhang, W.[Wei],
Yang, F.Z.[Fu-Zheng],
Wang, J.[Jing],
Li, G.[Ge],
Cross-Type Attribute Prediction For Point Cloud Compression,
ICIP22(2956-2960)
IEEE DOI
2211
Point cloud compression, Visualization, Image coding, Correlation,
Shape, Redundancy, Point cloud, attribute compression,
attribute variation
BibRef
Ma, C.A.[Chu-Ang],
Li, G.[Ge],
Zhang, Q.[Qi],
Shao, Y.T.[Yi-Ting],
Wang, J.[Jing],
Liu, S.[Shan],
Fast Recolor Prediction Scheme in Point Cloud Attribute Compression,
VCIP20(50-53)
IEEE DOI
2102
Transform coding, Geometry, Redundancy,
Correlation, Prediction algorithms, Interpolation, point cloud,
fast recolor
BibRef
Poursaeed, O.[Omid],
Jiang, T.X.[Tian-Xing],
Qiao, H.[Han],
Xu, N.[Nayun],
Kim, V.G.[Vladimir G.],
Self-Supervised Learning of Point Clouds via Orientation Estimation,
3DV20(1018-1028)
IEEE DOI
2102
Task analysis, Shape,
Predictive models, Solid modeling, Support vector machines,
Keypoint prediction
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
Xie, S.N.[Sai-Ning],
Gu, J.T.[Jia-Tao],
Guo, D.[Demi],
Qi, C.R.[Charles R.],
Guibas, L.J.[Leonidas J.],
Litany, O.[Or],
Pointcontrast: Unsupervised Pre-training for 3d Point Cloud
Understanding,
ECCV20(III:574-591).
Springer DOI
2012
BibRef
Liu, Z.[Ze],
Hu, H.[Han],
Cao, Y.[Yue],
Zhang, Z.[Zheng],
Tong, X.[Xin],
A Closer Look at Local Aggregation Operators in Point Cloud Analysis,
ECCV20(XXIII:326-342).
Springer DOI
2011
BibRef
Ghahremani, M.[Morteza],
Tiddeman, B.[Bernard],
Liu, Y.H.[Yong-Huai],
Behera, A.[Ardhendu],
Orderly Disorder in Point Cloud Domain,
ECCV20(XXVIII:494-509).
Springer DOI
2011
BibRef
Xu, C.F.[Chen-Feng],
Wu, B.[Bichen],
Wang, Z.[Zining],
Zhan, W.[Wei],
Vajda, P.[Peter],
Keutzer, K.[Kurt],
Tomizuka, M.[Masayoshi],
Squeezesegv3: Spatially-adaptive Convolution for Efficient Point-cloud
Segmentation,
ECCV20(XXVIII:1-19).
Springer DOI
2011
BibRef
Su, Z.[Zhe],
Bauer, M.[Martin],
Klassen, E.[Eric],
Gallivan, K.[Kyle],
Simplifying Transformations for a Family of Elastic Metrics on the
Space of Surfaces,
Diff-CVML20(3705-3714)
IEEE DOI
2008
Jermyn.
Shape, Space vehicles, Area measurement,
Extraterrestrial measurements, Manifolds, Tensile stress
BibRef
Thomas, H.[Hugues],
Qi, C.R.[Charles R.],
Deschaud, J.E.[Jean-Emmanuel],
Marcotegui, B.[Beatriz],
Goulette, F.[François],
Guibas, L.J.[Leonidas J.],
KPConv: Flexible and Deformable Convolution for Point Clouds,
ICCV19(6410-6419)
IEEE DOI
2004
computational geometry,
convolutional neural nets, learning (artificial intelligence),
BibRef
Liu, Y.,
Fan, B.,
Meng, G.,
Lu, J.,
Xiang, S.,
Pan, C.,
DensePoint: Learning Densely Contextual Representation for Efficient
Point Cloud Processing,
ICCV19(5238-5247)
IEEE DOI
2004
convolutional neural nets, data visualisation,
image representation, learning (artificial intelligence), Aggregates
BibRef
Mao, J.,
Wang, X.,
Li, H.,
Interpolated Convolutional Networks for 3D Point Cloud Understanding,
ICCV19(1578-1587)
IEEE DOI
2004
convolutional neural nets, feature extraction, Data structures,
image classification, image recognition, image representation.
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
Liu, X.,
Yan, M.,
Bohg, J.,
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences,
ICCV19(9245-9254)
IEEE DOI
2004
feature extraction, image representation, image segmentation,
image sequences, learning (artificial intelligence), Task analysis
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Region Techniques for Range and Surfaces .