11.2.3.4 Range Data, Point Cloud Processing and Analysis

Chapter Contents (Back)
Segmentation, Range. Interest Points, 3D. Point Cloud Processing. LiDAR. More than just features.
See also Features of Surfaces and Range Data, Ridges, Edges.
See also Point Cloud Classification.

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

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

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

Shen, Y.M.[Yang-Mei], Dai, W.R.[Wen-Rui], 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

Li, X.[Xin], Dai, W.R.[Wen-Rui], Li, S.H.[Shao-Hui], Li, C.L.[Cheng-Lin], Zou, J.[Junni], Xiong, H.K.[Hong-Kai],
3-D Point Cloud Attribute Compression with p-Laplacian Embedding Graph Dictionary Learning,
PAMI(46), No. 2, February 2024, pp. 975-993.
IEEE DOI 2401
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

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

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.M.[Nick M.],
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

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

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

Seo, H.[Hogeon], Noh, S.[Sangjun], Shin, S.[Sungho], Lee, K.[Kyoobin],
Probability propagation for faster and efficient point cloud segmentation using a neural network,
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 Networks: A Survey,
PAMI(45), No. 9, September 2023, pp. 11321-11339.
IEEE DOI 2309
BibRef

Wu, C.H.[Cheng-Hao], Hsu, C.F.[Chih-Fan], Hung, T.K.[Tzu-Kuan], Griwodz, C.[Carsten], Ooi, W.T.[Wei Tsang], 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 BibRef

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. BibRef

Zhu, M.[Minghan], 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 BibRef


Hong, C.Y.[Cheng-Yao], Chou, Y.Y.[Yu-Ying], Liu, T.L.[Tyng-Luh],
Attention Discriminant Sampling for Point Clouds,
ICCV23(14383-14394)
IEEE DOI 2401
BibRef

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

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

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
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

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 .


Last update:Feb 29, 2024 at 09:13:14