11.2.3.3 Point Cloud Classification, Recognition

Chapter Contents (Back)
Segmentation, Range. Feature Extraction. Segmentation, 3-D Data. Classification, 3-D Data. Point Cloud Recognition. Point Cloud Classification.
See also Range Data, Point Cloud Processing and Analysis. Features to use in CNN:
See also Point Cloud Processing for Neural Networks, Convolutional Neural Networks.

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

Zamorski, M.[Maciej], Zieba, M.[Maciej], Klukowski, P.[Piotr], Nowak, R.[Rafal], Kurach, K.[Karol], Stokowiec, W.[Wojciech], Trzcinski, T.[Tomasz],
Adversarial autoencoders for compact representations of 3D point clouds,
CVIU(193), 2020, pp. 102921.
Elsevier DOI 2003
Adversarial Autoencoders, Point Clouds, Deep Learning, Representation Learning, Neural Networks, Adversarial Learning BibRef

Bello, S.A.[Saifullahi Aminu], Yu, S.S.[Shang-Shu], Wang, C.[Cheng], Adam, J.M.[Jibril Muhmmad], Li, J.[Jonathan],
Review: Deep Learning on 3D Point Clouds,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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

Stypulkowski, M.[Michal], Kania, K.[Kacper], Zamorski, M.[Maciej], Zieba, M.[Maciej], Trzcinski, T.[Tomasz], Chorowski, J.[Jan],
Representing point clouds with generative conditional invertible flow networks,
PRL(150), 2021, pp. 26-32.
Elsevier DOI 2109
Deep learning, Generative models, Normalizing flows, Point cloud modeling 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

Boscaini, D.[Davide], Poiesi, F.[Fabio],
PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding,
IVC(137), 2023, pp. 104768.
Elsevier DOI 2309
BibRef
Earlier: A2, A1:
Distinctive 3D local deep descriptors,
ICPR21(5720-5727)
IEEE DOI 2105
3D deep learning, Point cloud understanding, Classification, Segmentation, Transfer learning. Neural networks, Registers, Clutter BibRef

Boscaini, D.[Davide], Poiesi, F.[Fabio],
3D Shape Segmentation with Geometric Deep Learning,
CIAP19(I:454-465).
Springer DOI 1909
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.Y.[Zi-Yu], 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

Štroner, 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

He, Y.[Yuan], Hu, G.[Guyue], Yu, S.[Shan],
Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification,
SPLetters(31), 2024, pp. 1059-1063.
IEEE DOI 2405
Point cloud compression, Training, Self-supervised learning, Semisupervised learning, Task analysis, Unsupervised learning, pseudo label BibRef

Shen, Z.Q.[Zhi-Qiang], Wang, L.G.[Long-Guang], Guo, Y.L.[Yu-Lan], Liu, Q.[Qiong], Zhou, X.[Xi],
Point Spatio-Temporal Pyramid Network for Point Cloud Video Understanding,
SPLetters(31), 2024, pp. 1209-1213.
IEEE DOI 2405
Point cloud compression, Robustness, Feature extraction, Aggregates, Semantics, Task analysis, Superresolution, spatio-temporal pyramid 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

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

Wang, Z.X.[Zhao-Xuan], Yu, Y.L.[Yun-Long], Li, X.Z.[Xian-Zhi],
Rethinking local-to-global representation learning for rotation-invariant point cloud analysis,
PR(154), 2024, pp. 110624.
Elsevier DOI Code:
WWW Link. 2406
Point cloud, Rotation invariance, Local-to-global, Deep learning BibRef

Xiao, Y.Z.[Yun-Zhe], Dou, Y.[Yong], Yang, S.[Shaowu],
PointBLIP: Zero-Training Point Cloud Classification Network Based on BLIP-2 Model,
RS(16), No. 13, 2024, pp. 2453.
DOI Link 2407
BibRef

Cao, X.[Xin], Wang, H.Y.[Hao-Yu], Zhu, Q.[Qiuquan], Wang, Y.F.[Yi-Fan], Liu, X.[Xiu], Li, K.[Kang], Su, L.[Linzhi],
PointStaClu: A Deep Point Cloud Clustering Method Based on Stable Cluster Discrimination,
RS(16), No. 13, 2024, pp. 2423.
DOI Link 2407
BibRef

Mei, G.F.[Guo-Feng], Saltori, C.[Cristiano], Ricci, E.[Elisa], Sebe, N.[Nicu], Wu, Q.[Qiang], Zhang, J.[Jian], Poiesi, F.[Fabio],
Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering,
IJCV(132), No. 8, August 2024, pp. 3251-3269.
Springer DOI 2408
Without data augmentation. BibRef

Esmorís, A.M.[Alberto M.], Weiser, H.[Hannah], Winiwarter, L.[Lukas], Cabaleiro, J.C.[Jose C.], Höfle, B.[Bernhard],
Deep learning with simulated laser scanning data for 3D point cloud classification,
PandRS(215), 2024, pp. 192-213.
Elsevier DOI 2408
Virtual laser scanning, LiDAR simulation, Point clouds, Machine learning, Point-wise classification, Leaf-wood segmentation BibRef

Fang, Z.B.[Zhong-Bin], Li, X.[Xia], Li, X.T.[Xiang-Tai], Zhao, S.[Shen], Liu, M.Y.[Meng-Yuan],
ModelNet-O: A large-scale synthetic dataset for occlusion-aware point cloud classification,
CVIU(246), 2024, pp. 104060.
Elsevier DOI Code:
WWW Link. 2408
Synthetic dataset, Point cloud classification, Occluded point cloud BibRef

Lai, L.L.[Lv-Long], Chen, J.[Jian], Lin, G.S.[Guo-Sheng], Wu, Q.Y.[Qing-Yao],
CMNet: Component-Aware Matching Network for Few-Shot Point Cloud Classification,
MultMed(26), 2024, pp. 9241-9251.
IEEE DOI 2409
Point cloud compression, Task analysis, Feature extraction, Measurement, Training, Training data, component matching BibRef

Wu, Q.X.[Qiu-Xia], Su, K.M.[Kun-Ming],
URINet: Unsupervised point cloud rotation invariant representation learning via semantic and structural reasoning,
CVIU(248), 2024, pp. 104136.
Elsevier DOI 2409
Unsupervised learning, Point cloud analysis, Rotation invariance BibRef

Zheng, Y.[Yu], Lu, J.W.[Ji-Wen], Duan, Y.[Yueqi], Zhou, J.[Jie],
Structural Relation Modeling of 3D Point Clouds,
IP(33), 2024, pp. 4867-4881.
IEEE DOI 2409
Point cloud compression, Solid modeling, Feature extraction, Semantics, Aggregates, Deep learning, Structural modeling, 3D deep learning BibRef

Ren, Y.J.[Ying-Jie], Xu, W.X.[Wen-Xue], Guo, Y.D.[Ya-Dong], Liu, Y.X.[Yan-Xiong], Tian, Z.[Ziwen], Lv, J.[Jing], Guo, Z.[Zhen], Guo, K.[Kai],
MLF-PointNet++: A Multifeature-Assisted and Multilayer Fused Neural Network for LiDAR-UAS Point Cloud Classification in Estuarine Areas,
RS(16), No. 17, 2024, pp. 3131.
DOI Link 2409
BibRef

Chu, X.[Xutao], Zhao, S.J.[Sheng-Jie], Dai, H.W.[Hong-Wei],
AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding,
RS(16), No. 21, 2024, pp. 4103.
DOI Link 2411
BibRef

Yang, J.[Jing], Zhu, X.T.[Xia-Tian], Bulat, A.[Adrian], Martinez, B.[Brais], Tzimiropoulos, G.[Georgios],
Knowledge Distillation Meets Open-Set Semi-supervised Learning,
IJCV(133), No. 1, January 2025, pp. 315-334.
Springer DOI 2501
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


Zhang, D.X.[Ding-Xin], Yu, J.H.[Jian-Hui], Xue, T.F.[Teng-Fei], Zhang, C.Y.[Chao-Yi], Liu, D.N.[Dong-Nan], Cai, W.D.[Wei-Dong],
Enhancing Robustness to Noise Corruption for Point Cloud Recognition via Spatial Sorting and Set-mixing Aggregation Module,
ACCV24(IX: 280-297).
Springer DOI 2412
BibRef

Jiang, J.[Jincen], Zhou, Q.Y.[Qian-Yu], Li, Y.H.[Yu-Hang], Lu, X.Q.[Xue-Quan], Wang, M.[Meili], Ma, L.Z.[Li-Zhuang], Chang, J.[Jian], Zhang, J.J.[Jian Jun],
DG-PIC: Domain Generalized Point-in-context Learning for Point Cloud Understanding,
ECCV24(VI: 455-474).
Springer DOI 2412
BibRef

Fan, L.[Linlong], Huang, Y.[Ye], Ge, Y.Q.[Yan-Qi], Li, W.[Wen], Duan, L.X.[Li-Xin],
Beyond Viewpoint: Robust 3d Object Recognition Under Arbitrary Views Through Joint Multi-part Representation,
ECCV24(LII: 291-309).
Springer DOI 2412
BibRef

Kim, Y.G.[Young-Gun], Lee, S.[Soomok],
3d Adaptive Structural Convolution Network for Domain-invariant Point Cloud Recognition,
ACCV24(IX: 419-435).
Springer DOI 2412
BibRef

Wang, C.S.[Chang-Shuo], Wu, M.Q.[Mei-Qing], Lam, S.K.[Siew-Kei], Ning, X.[Xin], Yu, S.S.[Shang-Shu], Wang, R.P.[Rui-Ping], Li, W.J.[Wei-Jun], Srikanthan, T.[Thambipillai],
Gpsformer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding,
ECCV24(VIII: 75-92).
Springer DOI 2412
BibRef

Zhang, S.J.[Sheng-Jun], Fei, X.[Xin], Duan, Y.[Yueqi],
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-Sensor Point Clouds,
CVPR24(20019-20028)
IEEE DOI 2410
Point cloud compression, Geometry, Representation learning, Laser radar, Network architecture, Cameras BibRef

Xu, X.W.[Xiu-Wei], Xia, C.[Chong], Wang, Z.W.[Zi-Wei], Zhao, L.Q.[Lin-Qing], Duan, Y.[Yueqi], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Memory-based Adapters for Online 3D Scene Perception,
CVPR24(21604-21613)
IEEE DOI 2410
Point cloud compression, Geometry, Adaptation models, Solid modeling, Computational modeling, Feature extraction BibRef

Qi, Z.Y.[Zhang-Yang], Fang, Y.[Ye], Sun, Z.Y.[Ze-Yi], Wu, X.Y.[Xiao-Yang], Wu, T.[Tong], Wang, J.Q.[Jia-Qi], Lin, D.[Dahua], Zhao, H.S.[Heng-Shuang],
GPT4Point: A Unified Framework for Point-Language Understanding and Generation,
CVPR24(26407-26417)
IEEE DOI 2410
Training, Point cloud compression, Solid modeling, Databases, Annotations, Shape, 3D Multimodal, Point Cloud Understanding, Point Cloud Generation BibRef

Lou, T.R.[Tian-Rui], Jia, X.J.[Xiao-Jun], Gu, J.D.[Jin-Dong], Liu, L.[Li], Liang, S.Y.[Si-Yuan], He, B.[Bangyan], Cao, X.C.[Xiao-Chun],
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds,
CVPR24(24326-24335)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Solid modeling, Deformation, Perturbation methods, Computational modeling BibRef

Wu, Y.[Yanhao], Zhang, T.[Tong], Ke, W.[Wei], Qiu, C.P.[Cong-Pei], Süsstrunk, S.[Sabine], Salzmann, M.[Mathieu],
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning Through Object Exchange,
CVPR24(23052-23061)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Representation learning, Semantics, Noise, Neural networks, Self-supervised learning, 3D vision BibRef

Melnyk, P.[Pavlo], Robinson, A.[Andreas], Felsberg, M.[Michael], Wadenbäck, M.[Mĺrten],
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis,
CVPR24(5620-5630)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Training, Solid modeling, Shape, Neurons, Vectors, spherical decision surface, point cloud, vector neurons BibRef

Sun, S.F.[Shuo-Feng], Rao, Y.M.[Yong-Ming], Lu, J.W.[Ji-Wen], Yan, H.B.[Hai-Bin],
X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition,
CVPR24(5074-5083)
IEEE DOI Code:
WWW Link. 2410
Point cloud compression, Solid modeling, Computational modeling, X3D, Feature extraction, Vectors BibRef

Huang, Y.[Yuanmin], Zhang, M.[Mi], Ding, D.[Daizong], Jiang, E.[Erling], Wang, Z.X.[Zhao-Xiang], Yang, M.[Min],
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification,
CVPR24(19779-19789)
IEEE DOI 2410
Point cloud compression, Adaptation models, Accuracy, Computational modeling, Noise, Artificial neural networks, Causal Inference BibRef

Peng, S.C.[Shi-Chong], Zhang, Y.S.[Yan-Shu], Li, K.[Ke],
PAPR in Motion: Seamless Point-level 3D Scene Interpolation,
CVPR24(21007-21016)
IEEE DOI Code:
WWW Link. 2410
Geometry, Point cloud compression, Interpolation, Deformation, Shape, Dynamics, Point-level 3D Scene Interpolation, Proximity Attention Point Rendering BibRef

Zhu, Y.H.[Yun-Hui], Chen, J.J.[Jia-Jing], Velipasalar, S.[Senem],
DepthVoting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism,
L3D24(699-707)
IEEE DOI 2410
Point cloud compression, Solid modeling, Head, Accuracy, Feature extraction, Data augmentation BibRef

Ren, H.[Huantao], Wang, J.[Jiyang], Yang, M.[Minmin], Velipasalar, S.[Senem],
PointOfView: A Multi-modal Network for Few-shot 3D Point Cloud Classification Fusing Point and Multi-view Image Features,
L3D24(784-793)
IEEE DOI 2410
Point cloud compression, Solid modeling, Shape, Computational modeling, point cloud, few-shot learning, multi-modal BibRef

Xu, Y.T.[Yi-Teng], Ye, K.C.[Ke-Cheng], Han, X.[Xiao], Ren, Y.M.[Yi-Ming], Zhu, X.G.[Xin-Ge], Ma, Y.X.[Yue-Xin],
A Unified Framework for Human-centric Point Cloud Video Understanding,
CVPR24(1155-1164)
IEEE DOI 2410
Point cloud compression, Codes, Semantics, Dynamics, Pose estimation BibRef

Mezghanni, M.[Mariem], Boulkenafed, M.[Malika], Ovsjanikov, M.[Maks],
RIVQ-VAE: Discrete Rotation-Invariant 3D Representation Learning,
3DV24(1382-1391)
IEEE DOI 2408
Geometry, Representation learning, Surface reconstruction, Codes, Shape, Computer architecture, Generative modeling, Point cloud completion BibRef

Wang, J.[Jie], Ding, L.[Lihe], Xu, T.F.[Ting-Fa], Dong, S.C.[Shao-Cong], Xu, X.L.[Xin-Li], Bai, L.[Long], Li, J.A.[Jian-An],
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions,
ICCV23(14284-14293)
IEEE DOI 2401
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.Y.[Zhi-Yu], 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.C.[Fu-Chen], Yao, T.[Ting], Qiu, Z.F.[Zhao-Fan], Li, L.S.[Lu-Song], 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

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

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 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 .


Last update:Jan 20, 2025 at 11:36:25