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Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT.
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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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 1534–1543
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A. Sharma, O. Grau, and M. Fritz, “Vconv-dae: Deep volumetric shape learning without object labels,” in European Conference on Computer Vision (ECCV) . Springer, 2016, pp. 236–250
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J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016, pp. 82–90
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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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 652–660
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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,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 30, 2017
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M. Gadelha, R. Wang, and S. Maji, “Multiresolution tree networks for 3d point cloud processing,” in European Conference on Computer Vision (ECCV) , 2018, pp. 103–118
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in International Conference on Machine Learning (ICML) , 2018, pp. 40–49
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M. Gadelha, R. Wang, and S. Maji, “Multiresolution tree networks for 3d point cloud processing,” in European Conference on Computer Vision (ECCV) , 2018, pp. 103–118
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in International Conference on Machine Learning (ICML) , 2018, pp. 40–49
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Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 206–215
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J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9397–9406
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J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9397–9406
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Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 206–215
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Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao, “Spidercnn: Deep learning on point sets with parameterized convolutional filters,” in European Conference on Computer Vision (ECCV) , 2018, pp. 87–102
2018
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J. Sauder and B. Sievers, “Self-supervised deep learning on point clouds by reconstructing space,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019
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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 Conference on Artificial Intelligence (AAAI) , vol. 33, no. 01, 2019, pp. 8376–8384
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P. Gao, Z. Jiang, H. You, P. Lu, S. C. Hoi, X. Wang, and H. Li, “Dynamic fusion with intra-and inter-modality attention flow for visual question answering,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 6639–6648
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M. A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung, “Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 1588–1597
2019
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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 IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 10 441–10 450
2019
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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 Conference on Artificial Intelligence (AAAI) , vol. 33, no. 01, 2019, pp. 8376–8384
2019
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Y. Zhao, T. Birdal, H. Deng, and F. Tombari, “3d point capsule networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 1009–1018
2019
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J. Sauder and B. Sievers, “Self-supervised deep learning on point clouds by reconstructing space,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, pp. 12 962–12 972, 2019
2019
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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 Transactions On Graphics (TOG) , vol. 38, no. 5, pp. 1–12, 2019
2019
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Y. Liu, B. Fan, G. Meng, J. Lu, S. Xiang, and C. Pan, “Densepoint: Learning densely contextual representation for efficient point cloud processing,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 5239–5248
2019
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Y. Liu, B. Fan, S. Xiang, and C. Pan, “Relation-shape convolutional neural network for point cloud analysis,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 8895–8904
2019
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S. Huang, Y. Xie, S.-C. Zhu, and Y. Zhu, “Spatio-temporal self-supervised representation learning for 3d point clouds,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 6535–6545
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L. Jing, L. Zhang, and Y. Tian, “Self-supervised feature learning by cross-modality and cross-view correspondences,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 1581–1591
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R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Transformer for semantic segmentation,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 7262–7272
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H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 16 259–16 268
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H. Chen, S. Luo, X. Gao, and W. Hu, “Unsupervised learning of geometric sampling invariant representations for 3d point clouds,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 893–903
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B. Eckart, W. Yuan, C. Liu, and J. Kautz, “Self-supervised learning on 3d point clouds by learning discrete generative models,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 8248–8257
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2021
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H. Wang, Q. Liu, X. Yue, J. Lasenby, and M. J. Kusner, “Unsupervised point cloud pre-training via occlusion completion,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 9782–9792
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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,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
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,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Closest in time.
H. Bao, L. Dong, and F. Wei, “Beit: Bert pre-training of image transformers,” International Conference on Learning Representations (ICLR) , 2022
2022
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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,” 2022
2022
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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,” 2022
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,” 2022
2022
Closest in time.