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Following the tremendous success of transformer in natural language processing and image understanding tasks, in this paper, we present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT) module.
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Y. Shen, C. Feng, Y. Yang, and D. Tian, “Mining point cloud local structures by kernel correlation and graph pooling,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4548–4557
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Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 206–215
2018
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J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9397–9406
2018
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G. Te, W. Hu, A. Zheng, and Z. Guo, “Rgcnn: Regularized graph cnn for point cloud segmentation,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 746–754
2018
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Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3523–3532
2019
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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 Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6411–6420
2019
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L. Wang, Y. Huang, Y. Hou, S. Zhang, and J. Shan, “Graph attention convolution for point cloud semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 296–10 305
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Y. Lin, Z. Yan, H. Huang, D. Du, L. Liu, S. Cui, and X. Han, “Fpconv: Learning local flattening for point convolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4293–4302
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K. Fujiwara and T. Hashimoto, “Neural implicit embedding for point cloud analysis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 734–11 743
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H. Lei, N. Akhtar, and A. Mian, “Spherical kernel for efficient graph convolution on 3d point clouds,” IEEE transactions on pattern analysis and machine intelligence , 2020
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