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The past few years have witnessed the great success and prevalence of self-supervised representation learning within the language and 2D vision communities.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in Proc. CVPR , 2015, pp. 1912–1920
1920
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S. Katz, A. Tal, and R. Basri, “Direct visibility of point sets,” in ACM SIGGRAPH , 2007, pp. 24–es
2007
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L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” JMLR , vol. 9, no. 11, 2008
2008
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A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Proc. CVPR , 2012, pp. 3354–3361
2012
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2015
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I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3d semantic parsing of large-scale indoor spaces,” in Proc. CVPR , 2016, pp. 1534–1543
2016
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J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,” Proc. NeurIPS , vol. 29, 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. CVPR , 2016, pp. 770–778
2016
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L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas, “A scalable active framework for region annotation in 3d shape collections,” ACM TOG , vol. 35, no. 6, pp. 1–12, 2016
2016
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proc. CVPR , 2017, pp. 652–660
2017
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Proc. NeurIPS , 2017, pp. 5105–5114
2017
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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proc. CVPR , 2017, pp. 5828–5839
2017
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Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on χ \chi -transformed points,” in Proc. NeurIPS , 2018, pp. 828–838
2018
Earlier work this paper cites.
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng, “Pu-net: Point cloud upsampling network,” in Proc. CVPR , 2018, pp. 2790–2799
2018
Earlier work this paper cites.
M. A. Uy and G. H. Lee, “Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition,” in Proc. CVPR , 2018, pp. 4470–4479
2018
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2018
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J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in Proc. CVPR , 2018, pp. 9397–9406
2018
Earlier work this paper cites.
Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in Proc. CVPR , 2018, pp. 206–215
2018
Earlier work this paper cites.
D. Valsesia, G. Fracastoro, and E. Magli, “Learning localized generative models for 3d point clouds via graph convolution,” in Proc. ICLR , 2018
2018
Earlier work this paper cites.
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in Proc. ICML , 2018, pp. 40–49
2018
Earlier work this paper cites.
N. Komodakis and S. Gidaris, “Unsupervised representation learning by predicting image rotations,” in Proc. ICLR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Guerrero, Y. Kleiman, M. Ovsjanikov, and N. J. Mitra, “Pcpnet learning local shape properties from raw point clouds,” in CGF , vol. 37, no. 2, 2018, pp. 75–85
2018
Earlier work this paper cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” ACM TOG , vol. 38, no. 5, pp. 1–12, 2019
2019
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Y. Liu, B. Fan, S. Xiang, and C. Pan, “Relation-shape convolutional neural network for point cloud analysis,” in Proc. CVPR , 2019, pp. 8895–8904
2019
Earlier work this paper cites.
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in Proc. ICCV , 2019, pp. 6411–6420
2019
Earlier work this paper cites.
Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proc. ICCV , 2019, pp. 3523–3532
2019
Earlier work this paper cites.
Y. Aoki, H. Goforth, R. A. Srivatsan, and S. Lucey, “Pointnetlk: Robust & efficient point cloud registration using pointnet,” in Proc. CVPR , 2019, pp. 7163–7172
2019
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proc. CVPR , 2019, pp. 770–779
2019
Cited alongside, same era.
K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su, “Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding,” in Proc. CVPR , 2019, pp. 909–918
2019
Cited alongside, same era.
J. Sauder and B. Sievers, “Self-supervised deep learning on point clouds by reconstructing space,” Proc. NeurIPS , vol. 32, 2019
2019
Cited alongside, same era.
W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in Proc. CVPR , 2019, pp. 9621–9630
2019
Cited alongside, same era.
S. Chen, C. Duan, Y. Yang, D. Li, C. Feng, and D. Tian, “Deep unsupervised learning of 3d point clouds via graph topology inference and filtering,” IEEE TIP , vol. 29, pp. 3183–3198, 2019
T. Xiang, C. Zhang, Y. Song, J. Yu, and W. Cai, “Walk in the cloud: Learning curves for point clouds shape analysis,” in Proc. ICCV , 2021, pp. 915–924
2021
Later among the works it cites.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proc. ICCV , 2021, pp. 16 259–16 268
2021
Later among the works it cites.
H. Wang, Q. Liu, X. Yue, J. Lasenby, and M. J. Kusner, “Unsupervised point cloud pre-training via occlusion completion,” in Proc. ICCV , 2021, pp. 9782–9792
2021
Later among the works it cites.
S. Huang, Y. Xie, S.-C. Zhu, and Y. Zhu, “Spatio-temporal self-supervised representation learning for 3d point clouds,” in Proc. ICCV , 2021, pp. 6535–6545
2021
Later among the works it cites.
Z. Zhang, R. Girdhar, A. Joulin, and I. Misra, “Self-supervised pretraining of 3d features on any point-cloud,” in Proc. ICCV , 2021, pp. 10 252–10 263
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2019
Cited alongside, same era.
K. Hassani and M. Haley, “Unsupervised multi-task feature learning on point clouds,” in Proc. ICCV , 2019, pp. 8160–8171
2019
Cited alongside, same era.
Y. Zhao, T. Birdal, H. Deng, and F. Tombari, “3d point capsule networks,” in Proc. CVPR , 2019, pp. 1009–1018
2019
Cited alongside, same era.
X. Liu, Z. Han, X. Wen, Y.-S. Liu, and M. Zwicker, “L2g auto-encoder: Understanding point clouds by local-to-global reconstruction with hierarchical self-attention,” in Proc. ACM MM , 2019, pp. 989–997
2019
Cited alongside, same era.
Z. Han, X. Wang, Y.-S. Liu, and M. Zwicker, “Multi-angle point cloud-vae: Unsupervised feature learning for 3d point clouds from multiple angles by joint self-reconstruction and half-to-half prediction,” in Proc. ICCV , 2019, pp. 10 441–10 450
2019
Cited alongside, same era.
Z. Han, M. Shang, Y.-S. Liu, and M. Zwicker, “View inter-prediction gan: Unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions,” in Proc. AAAI , vol. 33, no. 01, 2019, pp. 8376–8384
2019
Cited alongside, same era.
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan, “Pointflow: 3d point cloud generation with continuous normalizing flows,” in Proc. ICCV , 2019, pp. 4541–4550
2019
Cited alongside, same era.
P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax.” in Proc. ICLR , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE TKDE , 2021
2021
Later among the works it cites.
B. Du, X. Gao, W. Hu, and X. Li, “Self-contrastive learning with hard negative sampling for self-supervised point cloud learning,” in Proc. ACM MM , 2021, pp. 3133–3142
2021
Later among the works it cites.
Y. Zeng, Y. Qian, Z. Zhu, J. Hou, H. Yuan, and Y. He, “Corrnet3d: Unsupervised end-to-end learning of dense correspondence for 3d point clouds,” in Proc. CVPR , 2021, pp. 6052–6061
2021
Later among the works it cites.
W. Feng, J. Zhang, H. Cai, H. Xu, J. Hou, and H. Bao, “Recurrent multi-view alignment network for unsupervised surface registration,” in Proc. CVPR , 2021, pp. 10 297–10 307
2021
Later among the works it cites.
M. Xu, R. Ding, H. Zhao, and X. Qi, “Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds,” in Proc. CVPR , 2021, pp. 3173–3182
2021
Later among the works it cites.
M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu, “Pct: Point cloud transformer,” CVM , vol. 7, no. 2, pp. 187–199, 2021
2021
Later among the works it cites.
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, “Learning semantic segmentation of large-scale point clouds with random sampling,” IEEE TPAMI , 2021
2021
Later among the works it cites.
M. Afham, I. Dissanayake, D. Dissanayake, A. Dharmasiri, K. Thilakarathna, and R. Rodrigo, “Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding,” in Proc. CVPR , 2022, pp. 9902–9912
2022
Closest in time.
L. Jing, P. Vincent, Y. LeCun, and Y. Tian, “Understanding dimensional collapse in contrastive self-supervised learning,” in Proc. ICLR , 2022
2022
Closest in time.
G. Sharma, K. Yin, S. Maji, E. Kalogerakis, O. Litany, and S. Fidler, “Mvdecor: Multi-view dense correspondence learning for fine-grained 3d segmentation,” in Proc. ECCV , 2022, pp. 550–567
2022
Closest in time.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proc. CVPR , 2022, pp. 16 000–16 009
2022
Closest in time.
X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu, “Point-bert: Pre-training 3d point cloud transformers with masked point modeling,” in Proc. CVPR , 2022, pp. 19 313–19 322
2022
Closest in time.
Y. Pang, W. Wang, F. E. Tay, W. Liu, Y. Tian, and L. Yuan, “Masked autoencoders for point cloud self-supervised learning,” in Proc. ECCV , 2022, pp. 604–621
2022
Closest in time.
H. Liu, M. Cai, and Y. J. Lee, “Masked discrimination for self-supervised learning on point clouds,” in Proc. ECCV , 2022, pp. 657–675
2022
Closest in time.
R. Zhang, Z. Guo, P. Gao, R. Fang, B. Zhao, D. Wang, Y. Qiao, and H. Li, “Point-m2AE: Multi-scale masked autoencoders for hierarchical point cloud pre-training,” in Proc. NeurIPS , 2022
2022
Closest in time.
G. Qian, Y. Li, H. Peng, J. Mai, H. Hammoud, M. Elhoseiny, and B. Ghanem, “Pointnext: Revisiting pointnet++ with improved training and scaling strategies,” Proc. NeurIPS , vol. 35, pp. 23 192–23 204, 2022
2022
Closest in time.
2022
Closest in time.
Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-nerf: Point-based neural radiance fields,” in Proc. CVPR , 2022, pp. 5438–5448
2022
Closest in time.
G. Metzer, R. Hanocka, R. Giryes, N. J. Mitra, and D. Cohen-Or, “Z2p: Instant visualization of point clouds,” in Eurographics , vol. 41, no. 2, 2022, pp. 461–471
2022
Closest in time.
Q. Zhang, J. Hou, and Y. Qian, “Pointmcd: Boosting deep point cloud encoders via multi-view cross-modal distillation for 3d shape recognition,” IEEE TMM , 2023
2023
Closest in time.
Q. Li, H. Feng, K. Shi, Y. Gao, Y. Fang, Y.-S. Liu, and Z. Han, “Shs-net: Learning signed hyper surfaces for oriented normal estimation of point clouds,” in Proc. CVPR , 2023, pp. 13 591–13 600
2023
Closest in time.
R. Xu, Z. Dou, N. Wang, S. Xin, S. Chen, M. Jiang, X. Guo, W. Wang, and C. Tu, “Globally consistent normal orientation for point clouds by regularizing the winding-number field,” ACM TOG , vol. 42, no. 4, pp. 1–15, 2023
2023
Closest in time.