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We propose simple yet effective improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks (GNNs) for 3D point cloud processing.
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2016
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T. Furuya and R. Ohbuchi, “Deep aggregation of local 3d geometric features for 3d model retrieval.” in BMVC , 2016
2016
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A. Ioannidou, E. Chatzilari, S. Nikolopoulos, and I. Kompatsiaris, “Deep learning advances in computer vision with 3d data: A survey,” ACM Computing Surveys (CSUR) , vol. 50, no. 2, p. 20, 2017
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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,” Proceedings of the IEEE conference on computer vision and pattern recognition , 2017
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5828–5839
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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 , 2017
2017
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R. Klokov and V. Lempitsky, “Escape from cells: Deep kd-networks for the recognition of 3d point cloud models,” Proceedings of the IEEE International Conference on Computer Vision , 2017
2017
Cited alongside, same era.
X. Roynard, J.-E. Deschaud, and F. Goulette, “Paris-lille-3d: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification,” The International Journal of Robotics Research , vol. 37, no. 6, pp. 545–557, 2018
2018
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M. Atzmon, H. Maron, and Y. Lipman, “Point convolutional neural networks by extension operators,” ACM Transactions on Graphics , 2018
2018
Cited alongside, same era.
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” in Advances in Neural Information Processing Systems , 2018, pp. 820–830
2018
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F. Engelmann et al., “Dilated point convolutions: On the receptive field of point convolutions,” ICRA , 2019
2019
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Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
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2020
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X. Yan, C. Zheng, Z. Li, S. Wang, and S. Cui, “Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5589–5598
2020
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W. Liu, J. Sun, W. Li, T. Hu, and P. Wang, “Deep learning on point clouds and its application: A survey,” Sensors , vol. 19, no. 19, p. 4188, 2019
2019
Cited alongside, same era.
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, p. 146, 2019
2019
Cited alongside, same era.
S. Srivastava and B. Lall, “Deeppoint3d: Learning discriminative local descriptors using deep metric learning on 3d point clouds,” Pattern Recognition Letters , 2019
2019
Cited alongside, same era.
Y. Liu, B. Fan, S. Xiang, and C. Pan, “Relation-shape convolutional neural network for point cloud analysis,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8895–8904
2019
Cited alongside, same era.
S. Kumawat and S. Raman, “Lp-3dcnn: Unveiling local phase in 3d convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4903–4912
2019
Cited alongside, same era.
C. Choy et al., “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3075–3084
2019
Cited alongside, same era.
G. Li, M. Muller, A. Thabet, and B. Ghanem, “Deepgcns: Can gcns go as deep as cnns?” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9267–9276
2019
Cited alongside, same era.
L. Wang, Y. Huang, Y. Hou, S. Zhang, and J. Shan, “Graph attention convolution for point cloud semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 296–10 305
2019
Cited alongside, same era.
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
2020
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——, “Seggcn: Efficient 3d point cloud segmentation with fuzzy spherical kernel,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 611–11 620
2020
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M. Xu, Z. Zhou, and Y. Qiao, “Geometry sharing network for 3d point cloud classification and segmentation.” in AAAI , 2020, pp. 12 500–12 507
2020
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Y. Lyu, X. Huang, and Z. Zhang, “Learning to segment 3d point clouds in 2d image space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 255–12 264
2020
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A. Boulch, “Convpoint: Continuous convolutions for point cloud processing,” Computers & Graphics , 2020
2020
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A. Kundu, X. Yin, A. Fathi, D. Ross, B. Brewington, T. Funkhouser, and C. Pantofaru, “Virtual multi-view fusion for 3d semantic segmentation,” ECCV , 2020
2020
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Z. Hu, M. Zhen, X. Bai, H. Fu, and C.-l. Tai, “Jsenet: Joint semantic segmentation and edge detection network for 3d point clouds,” ECCV , 2020
2020
Later among the works it cites.
F. Zhang, J. Fang, B. Wah, and P. Torr, “Deep fusionnet for point cloud semantic segmentation,” ECCV , 2020
2020
Later among the works it cites.
J. Schult, F. Engelmann, T. Kontogianni, and B. Leibe, “Dualconvmesh-net: Joint geodesic and euclidean convolutions on 3d meshes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8612–8622
2020
Later among the works it cites.
Z. Liu, H. Hu, Y. Cao, Z. Zhang, and X. Tong, “A closer look at local aggregation operators in point cloud analysis,” ECCV , 2020
2020
Later among the works it cites.
K. K. Parida, S. Srivastava, and G. Sharma, “Beyond image to depth: Improving depth prediction using echoes,” in CVPR , 2021
2021
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