11.2.3.3 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.

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


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.[Stefanos],
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 .


Last update:Jan 29, 2023 at 20:54:24