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Lin, W.,
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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
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Elsevier DOI
2002
MLS point clouds, Sequential slice representation,
Shape recognition, Shape retrieval, Deep learning, Embedding attention strategy
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Generalized Sparse Convolutional Neural Networks for Semantic
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IEEE DOI
2007
Laser radar, Clustering algorithms,
Bipartite graph, Roads, Feature extraction, Symmetric matrices,
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Multi-Scale Structured Dictionary Learning for 3-D Point Cloud
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CirSysVideo(31), No. 7, July 2021, pp. 2792-2807.
IEEE DOI
2107
Encoding, Geometry, Transforms,
Dictionaries, Machine learning, Sparse matrices, hierarchical sparse coding
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2401
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Chen, C.F.[Chuan-Fa],
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2107
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Gao, Y.[Yue],
Rotation-Invariant Point Cloud Representation for 3-D Model
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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
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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
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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
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Lu, D.[Dening],
Xie, Q.[Qian],
Gao, K.[Kyle],
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Li, J.[Jonathan],
3DCTN: 3D Convolution-Transformer Network for Point Cloud
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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
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Qiu, S.[Shi],
Anwar, S.[Saeed],
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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
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Huang, T.X.[Tian-Xin],
Chen, J.[Jun],
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Fast Point Cloud Sampling Network,
PRL(164), 2022, pp. 216-223.
Elsevier DOI
2212
3D Point Cloud, Neural Network, Sampling
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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
Yang, Q.[Qi],
Zhang, Y.J.[Yu-Jie],
Chen, S.[Siheng],
Xu, Y.L.[Yi-Ling],
Sun, J.[Jun],
Ma, Z.[Zhan],
MPED: Quantifying Point Cloud Distortion Based on Multiscale
Potential Energy Discrepancy,
PAMI(45), No. 5, May 2023, pp. 6037-6054.
IEEE DOI
2304
Distortion, Point cloud compression, Task analysis,
Potential energy, Feature extraction, point cloud
BibRef
Tang, X.[Xikai],
Huang, F.Z.[Fang-Zheng],
Li, C.[Chao],
Ban, D.[Dayan],
A survey on end-to-end point cloud learning,
IET-IPR(17), No. 5, 2023, pp. 1307-1321.
DOI Link
2304
deep learning, end-to-end, point cloud,
object detection and tracking, segmentation, shape classification
BibRef
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Shin, S.[Sungho],
Lee, K.[Kyoobin],
Probability propagation for faster and efficient point cloud
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PRL(170), 2023, pp. 24-31.
Elsevier DOI
2306
Neural network, Point cloud segmentation,
Probability propagation, Stochastic upsampling, Sampling method
BibRef
Xiao, A.[Aoran],
Huang, J.X.[Jia-Xing],
Guan, D.[Dayan],
Zhang, X.Q.[Xiao-Qin],
Lu, S.J.[Shi-Jian],
Shao, L.[Ling],
Unsupervised Point Cloud Representation Learning With Deep Neural
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PAMI(45), No. 9, September 2023, pp. 11321-11339.
IEEE DOI
2309
BibRef
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Hsu, C.F.[Chih-Fan],
Hung, T.K.[Tzu-Kuan],
Griwodz, C.[Carsten],
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Hsu, C.H.[Cheng-Hsin],
Quantitative Comparison of Point Cloud Compression Algorithms With
PCC Arena,
MultMed(25), 2023, pp. 3073-3088.
IEEE DOI
2309
Code, Point Cloud. we propose an open-source benchmark platform called PCC Arena
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Xiong, J.[Jian],
Gao, H.[Hao],
Wang, M.[Miaohui],
Li, H.L.[Hong-Liang],
Ngan, K.N.[King Ngi],
Lin, W.S.[Wei-Si],
Efficient Geometry Surface Coding in V-PCC,
MultMed(25), 2023, pp. 3329-3342.
IEEE DOI
2309
video-based point cloud compression.
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Zhu, M.H.[Ming-Han],
Ghaffari, M.[Maani],
Clark, W.A.[William A.],
Peng, H.[Huei],
E2PN: Efficient SE(3)-Equivariant Point Network,
CVPR23(1223-1232)
IEEE DOI
2309
BibRef
Xie, T.[Tao],
Wang, S.G.[Shi-Guang],
Wang, K.[Ke],
Yang, L.Q.[Lin-Qi],
Jiang, Z.Q.[Zhi-Qiang],
Zhang, X.C.[Xing-Cheng],
Dai, K.[Kun],
Li, R.F.[Rui-Feng],
Cheng, J.[Jian],
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once,
CVPR23(1233-1243)
IEEE DOI
2309
BibRef
Reis, N.[Nuno],
Machado-da Silva, J.[José],
Correia, M.V.[Miguel Velhote],
An Introduction to the Evaluation of Perception Algorithms and LiDAR
Point Clouds Using a Copula-Based Outlier Detector,
RS(15), No. 18, 2023, pp. 4570.
DOI Link
2310
BibRef
Huang, Z.X.[Zhuo-Xu],
Zhao, Z.Y.[Zhi-You],
Li, B.H.[Bang-Huai],
Han, J.G.[Jun-Gong],
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local
Context Propagation in Transformers,
CirSysVideo(33), No. 9, September 2023, pp. 4985-4996.
IEEE DOI
2310
BibRef
Han, X.F.[Xian-Feng],
Jin, Y.F.[Yi-Fei],
Cheng, H.X.[Hui-Xian],
Xiao, G.Q.[Guo-Qiang],
Dual Transformer for Point Cloud Analysis,
MultMed(25), 2023, pp. 5638-5648.
IEEE DOI
2311
BibRef
Roodaki, H.[Hoda],
Bojnordi, M.N.[Mahdi Nazm],
Compressed Geometric Arrays for Point Cloud Processing,
MultMed(25), 2023, pp. 8204-8211.
IEEE DOI
2312
BibRef
Liu, D.[Daizong],
Hu, W.[Wei],
Li, X.[Xin],
Robust Geometry-Dependent Attack for 3D Point Clouds,
MultMed(26), 2024, pp. 2866-2877.
IEEE DOI
2402
Point cloud compression, Perturbation methods, Geometry,
Solid modeling, Feature extraction, Topology, Disentanglement,
point cloud processing
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Kambhamettu, C.[Chandra],
3DSAINT Representation for 3D Point Clouds,
CV4MR23(2765-2774)
IEEE DOI
2309
BibRef
de Silva-Edirimuni, D.[Dasith],
Lu, X.Q.[Xue-Quan],
Shao, Z.W.[Zhi-Wen],
Li, G.[Gang],
Robles-Kelly, A.[Antonio],
He, Y.[Ying],
IterativePFN: True Iterative Point Cloud Filtering,
CVPR23(13530-13539)
IEEE DOI
2309
BibRef
Lin, H.J.[Hao-Jia],
Zheng, X.[Xiawu],
Li, L.[Lijiang],
Chao, F.[Fei],
Wang, S.S.[Shan-Shan],
Wang, Y.[Yan],
Tian, Y.H.[Yong-Hong],
Ji, R.R.[Rong-Rong],
Meta Architecture for Point Cloud Analysis,
CVPR23(17682-17691)
IEEE DOI
2309
BibRef
Zhang, R.[Renrui],
Wang, L.[Liuhui],
Wang, Y.[Yali],
Gao, P.[Peng],
Li, H.S.[Hong-Sheng],
Shi, J.B.[Jian-Bo],
Starting from Non-Parametric Networks for 3D Point Cloud Analysis,
CVPR23(5344-5353)
IEEE DOI
2309
BibRef
Zhang, J.H.[Jing-Huai],
Jia, J.[Jinyuan],
Liu, H.B.[Hong-Bin],
Gong, N.Z.Q.[Neil Zhen-Qiang],
PointCert: Point Cloud Classification with Deterministic Certified
Robustness Guarantees,
CVPR23(9496-9505)
IEEE DOI
2309
BibRef
Chen, C.[Chao],
Liu, X.[Xinhao],
Li, Y.M.[Yi-Ming],
Ding, L.[Li],
Feng, C.[Chen],
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization,
CVPR23(9306-9316)
IEEE DOI
2309
BibRef
Wu, X.Y.[Xiao-Yang],
Wen, X.[Xin],
Liu, X.H.[Xi-Hui],
Zhao, H.S.[Heng-Shuang],
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D
Representation Learning,
CVPR23(9415-9424)
IEEE DOI
2309
BibRef
Deng, X.[Xin],
Zhang, W.Y.[Wen-Yu],
Ding, Q.[Qing],
Zhang, X.M.[Xin-Ming],
PointVector: A Vector Representation In Point Cloud Analysis,
CVPR23(9455-9465)
IEEE DOI
2309
BibRef
Liu, K.C.[Kang-Cheng],
Xiao, A.[Aoran],
Zhang, X.Q.[Xiao-Qin],
Lu, S.J.[Shi-Jian],
Shao, L.[Ling],
FAC: 3D Representation Learning via Foreground Aware Feature Contrast,
CVPR23(9476-9485)
IEEE DOI
2309
BibRef
Lu, T.[Tao],
Ding, X.[Xiang],
Liu, H.S.[Hai-Song],
Wu, G.S.[Gang-Shan],
Wang, L.M.[Li-Min],
LinK: Linear Kernel for LiDAR-based 3D Perception,
CVPR23(1105-1115)
IEEE DOI
2309
BibRef
Hess, G.[Georg],
Jaxing, J.[Johan],
Svensson, E.[Elias],
Hagerman, D.[David],
Petersson, C.[Christoffer],
Svensson, L.[Lennart],
Masked Autoencoder for Self-Supervised Pre-training on Lidar Point
Clouds,
Pretrain23(350-359)
IEEE DOI
2302
Point cloud compression, Training, Laser radar, Annotations,
Tracking, Computational modeling
BibRef
Zhang, R.R.[Ren-Rui],
Wang, L.[Liuhui],
Guo, Z.Y.[Zi-Yu],
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
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
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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
Xu, J.Y.[Jian-Yun],
Tang, X.[Xin],
Zhu, Y.S.[Yu-Shi],
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
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
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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
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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
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 .