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Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.
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M. Dominguez, R. Dhamdhere, A. Petkar, S. Jain, S. Sah, and R. Ptucha, “General-purpose deep point cloud feature extractor,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2018, pp. 1972–1981
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R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (fpfh) for 3d registration,” in 2009 IEEE international conference on robotics and automation . IEEE, 2009, pp. 3212–3217
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2009
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Q. Mérigot, M. Ovsjanikov, and L. J. Guibas, “Voronoi-based curvature and feature estimation from point clouds,” IEEE Trans. Vis. Comput. Graph. , vol. 17, no. 6, pp. 743–756, 2010
2010
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M. Aubry, U. Schlickewei, and D. Cremers, “The wave kernel signature: A quantum mechanical approach to shape analysis,” in 2011 IEEE international conference on computer vision workshops (ICCV workshops) . IEEE, 2011, pp. 1626–1633
2011
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
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Y. Guo, F. Sohel, M. Bennamoun, M. Lu, and J. Wan, “Rotational projection statistics for 3d local surface description and object recognition,” International journal of computer vision , vol. 105, no. 1, pp. 63–86, 2013
2013
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H. Huang, S. Wu, M. Gong, D. Cohen-Or, U. Ascher, and H. R. Zhang, “Edge-aware point set resampling,” ACM Trans. Graph. , vol. 32, no. 1, p. 9, 2013
2013
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H. Yang and I. Patras, “Privileged information-based conditional regression forest for facial feature detection,” in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) . IEEE, 2013, pp. 1–6
2013
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V. Sharmanska, N. Quadrianto, and C. H. Lampert, “Learning to rank using privileged information,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 825–832
2013
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J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral networks and locally connected networks on graphs,” in International Conference on Learning Representations (ICLR2014) , 2014, pp. http–openreview
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2014
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2015
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C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1422–1430
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2015
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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,” in Advances in neural information processing systems , 2016, pp. 82–90
2016
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M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems , 2016, pp. 3844–3852
2016
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2016
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A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with exemplar convolutional neural networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 9, pp. 1734–1747, Sep. 2016
2016
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2018
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C. Gan, B. Gong, K. Liu, H. Su, and L. J. Guibas, “Geometry guided convolutional neural networks for self-supervised video representation learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 5589–5597
2018
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M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash, “Boosting self-supervised learning via knowledge transfer,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9359–9367
2018
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D. Novotny, S. Albanie, D. Larlus, and A. Vedaldi, “Self-supervised learning of geometrically stable features through probabilistic introspection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3637–3645
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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 Trans. Graph. , vol. 35, no. 6, pp. 1–12, 2016
2016
Cited alongside, same era.
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1534–1543
2016
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660
2017
Cited alongside, same era.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in neural information processing systems , 2017, pp. 5099–5108
2017
Cited alongside, same era.
B. Fernando, H. Bilen, E. Gavves, and S. Gould, “Self-supervised video representation learning with odd-one-out networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3636–3645
2017
Cited alongside, same era.
X. Wang, K. He, and A. Gupta, “Transitive invariance for self-supervised visual representation learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1329–1338
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan, “Learning features by watching objects move,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2701–2710
2017
Cited alongside, same era.
2018
Later among the works it cites.
X. Zhou, F. Shen, L. Liu, W. Liu, L. Nie, Y. Yang, and H. T. Shen, “Graph convolutional network hashing,” IEEE T. Cybern. , 2018
2018
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L. Wu, Y. Wang, X. Li, and J. Gao, “Deep attention-based spatially recursive networks for fine-grained visual recognition,” IEEE T. Cybern. , vol. 49, no. 5, pp. 1791–1802, 2018
2018
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M. Tatarchenko, J. Park, V. Koltun, and Q.-Y. Zhou, “Tangent convolutions for dense prediction in 3d,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3887–3896
2018
Later among the works it cites.
W. Wang, R. Yu, Q. Huang, and U. Neumann, “Sgpn: Similarity group proposal network for 3d point cloud instance segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2569–2578
2018
Later among the works it cites.
Q. Huang, W. Wang, and U. Neumann, “Recurrent slice networks for 3d segmentation of point clouds,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2626–2635
2018
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2019
Later among the works it 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 Trans. Graph. , vol. 38, no. 5, pp. 1–12, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
K. Wang, K. Chen, and K. Jia, “Deep cascade generation on point sets,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI) , 2019, pp. 3726–3732
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Pan, R. Hu, S. Fung, G. Long, J. Jiang, and C. Zhang, “Learning graph embedding with adversarial training methods,” IEEE T. Cybern. , pp. 1–13, 2019
2019
Later among the works it cites.
Y. Zhang, K. Jia, and Z. Wang, “Part-aware fine-grained object categorization using weakly supervised part detection network,” IEEE Trans. Multimedia , 2019
2019
Later among the works it cites.
S. Li, K. Jia, Y. Wen, T. Liu, and D. Tao, “Orthogonal deep neural networks,” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1–1, 2019
2019
Later among the works it cites.
C. Doersch and A. Zisserman, “Multi-task self-supervised visual learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2051–2060
2060
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