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The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field.
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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 CVPR , 2019, pp. 6411–6420
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in CVPR , 2019, pp. 9621–9630
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in CVPR , 2019, pp. 12 697–12 705
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J. Yang, Q. Zhang, B. Ni, L. Li, J. Liu, M. Zhou, and Q. Tian, “Modeling point clouds with self-attention and gumbel subset sampling,” in CVPR , 2019, pp. 3323–3332
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Y. Li, L. Ma, Z. Zhong, F. Liu, M. A. Chapman, D. Cao, and J. Li, “Deep learning for lidar point clouds in autonomous driving: A review,” TNNLS , vol. 32, no. 8, pp. 3412–3432, 2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV , 2020, pp. 213–229
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Q. Xie, Y.-K. Lai, J. Wu, Z. Wang, Y. Zhang, K. Xu, and J. Wang, “Mlcvnet: Multi-level context votenet for 3d object detection,” in CVPR , 2020, pp. 10 447–10 456
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L. Huang, J. Tan, J. Liu, and J. Yuan, “Hand-transformer: Non-autoregressive structured modeling for 3d hand pose estimation,” in ECCV , 2020, pp. 17–33
2020
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L. Huang, J. Tan, J. Meng, J. Liu, and J. Yuan, “Hot-net: Non-autoregressive transformer for 3d hand-object pose estimation,” in ACM MM , 2020, pp. 3136–3145
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Y. He, R. Yan, K. Fragkiadaki, and S.-I. Yu, “Epipolar transformers,” in CVPR , 2020, pp. 7779–7788
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W. Mao, M. Liu, and M. Salzmann, “History repeats itself: Human motion prediction via motion attention,” in ECCV , 2020, pp. 474–489
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Y. Cai, L. Huang, Y. Wang, T.-J. Cham, J. Cai, J. Yuan, J. Liu, X. Yang, Y. Zhu, X. Shen, D. Liu, J. Liu, and N. M. Thalmann, “Learning progressive joint propagation for human motion prediction,” in ECCV , 2020, pp. 226–242
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2020
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR , 2020, pp. 11 621–11 631
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Z. Zhang, B. Sun, H. Yang, and Q. Huang, “H3dnet: 3d object detection using hybrid geometric primitives,” in ECCV , 2020, pp. 311–329
2020
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S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,” in CVPR , 2020, pp. 10 529–10 538
2020
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in CVPR , 2020, pp. 11 040–11 048
2020
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J. Wang, S. Yan, Y. Xiong, and D. Lin, “Motion guided 3d pose estimation from videos,” in ECCV , 2020, pp. 764–780
2020
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2021
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D. Fernandes, A. Silva, R. Névoa, C. Simões, D. Gonzalez, M. Guevara, P. Novais, J. Monteiro, and P. Melo-Pinto, “Point-cloud based 3d object detection and classification methods for self-driving applications: A survey and taxonomy,” Information Fusion , vol. 68, pp. 161–191, 2021
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X. Wen, P. Xiang, Z. Han, Y.-P. Cao, P. Wan, W. Zheng, and Y.-S. Liu, “Pmp-net: Point cloud completion by learning multi-step point moving paths,” in CVPR , 2021, pp. 7443–7452
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