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Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in IEEE conference on computer vision and pattern recognition , 2015, pp. 1912–1920
1920
Earlier work this paper cites.
J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in International Conference on Knowledge Discovery & Data Mining , 2018, pp. 1930–1939
1939
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in International Conference on Learning Representations , 2015, pp. 1–15
2015
Earlier work this paper cites.
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in IEEE international conference on computer vision , 2015, pp. 945–953
2015
Earlier work this paper cites.
D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2015, pp. 922–928
2015
Earlier work this paper cites.
P. Wang, Y. Liu, and X. Tong, “Mesh denoising via cascaded normal regression,” ACM Trans. Graph. , vol. 35, no. 6, pp. 232:1–232:12, 2016
2016
Earlier work this paper cites.
X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool, “Dynamic filter networks,” Advances in neural information processing systems , vol. 29, pp. 667–675, 2016
2016
Earlier work this paper cites.
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas, “Volumetric and multi-view cnns for object classification on 3d data,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5648–5656
2016
Earlier work this paper cites.
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 Transactions on Graphics , vol. 35, no. 6, pp. 1–12, 2016
2016
Earlier work this paper cites.
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 Conference on Computer Vision and Pattern Recognition , 2016, pp. 1534–1543
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1874–1883
2016
Earlier work this paper cites.
G. Riegler, A. Osman Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in IEEE conference on computer vision and pattern recognition , 2017, pp. 3577–3586
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 77–85
2017
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” stat , vol. 1050, p. 20, 2017
2017
Earlier work this paper cites.
J. Gehring, M. Auli, D. Grangier, and Y. N. Dauphin, “A convolutional encoder model for neural machine translation,” in Annual Meeting of the Association for Computational Linguistics , 2017, pp. 123–135
2017
Earlier work this paper cites.
E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri, “3d shape segmentation with projective convolutional networks,” in IEEE conference on computer vision and pattern recognition , 2017, pp. 3779–3788
2017
Earlier work this paper cites.
P.-S. Wang, Y. Liu, Y.-X. Guo, C.-Y. Sun, and X. Tong, “O-cnn: Octree-based convolutional neural networks for 3d shape analysis,” ACM Transactions on Graphics , vol. 36, no. 4, pp. 1–11, 2017
2017
Earlier work this paper cites.
R. Klokov and V. Lempitsky, “Escape from cells: Deep kd-networks for the recognition of 3d point cloud models,” in IEEE International Conference on Computer Vision , 2017, pp. 863–872
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Annual Conference on Neural Information Processing Systems , 2017, pp. 5099–5108
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2017, pp. 1–14
2017
Earlier work this paper cites.
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” in IEEE conference on computer vision and pattern recognition , 2017, pp. 5115–5124
2017
Earlier work this paper cites.
M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in IEEE conference on computer vision and pattern recognition , 2017, pp. 3693–3702
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with warm restarts,” in International Conference on Learning Representations , 2017, pp. 1–16
2017
Earlier work this paper cites.
L. Tchapmi, C. Choy, I. Armeni, J. Gwak, and S. Savarese, “Segcloud: Semantic segmentation of 3d point clouds,” in International conference on 3D vision , 2017, pp. 537–547
2017
Earlier work this paper cites.
Y. Feng, Z. Zhang, X. Zhao, R. Ji, and Y. Gao, “Gvcnn: Group-view convolutional neural networks for 3d shape recognition,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 264–272
2018
Earlier work this paper cites.
T. Yu, J. Meng, and J. Yuan, “Multi-view harmonized bilinear network for 3d object recognition,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 186–194
2018
Earlier work this paper cites.
T. Le and Y. Duan, “Pointgrid: A deep network for 3d shape understanding,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 9204–9214
2018
Earlier work this paper cites.
Q. Huang, W. Wang, and U. Neumann, “Recurrent slice networks for 3d segmentation of point clouds,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2626–2635
2018
Earlier work this paper cites.
M. Gadelha, R. Wang, and S. Maji, “Multiresolution tree networks for 3d point cloud processing,” in European Conference on Computer Vision , 2018, pp. 103–118
2018
Earlier work this paper cites.
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on χ \chi -transformed points,” in International Conference on Neural Information Processing Systems , 2018, pp. 828–838
2018
Earlier work this paper cites.
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 , 2018, pp. 87–102
2018
Earlier work this paper cites.
M. Atzmon, H. Maron, and Y. Lipman, “Point convolutional neural networks by extension operators,” ACM Trans. Graph. , vol. 37, no. 4, p. 71, 2018
2018
Earlier work this paper cites.
Y. Shen, C. Feng, Y. Yang, and D. Tian, “Mining point cloud local structures by kernel correlation and graph pooling,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 4548–4557
2018
Earlier work this paper cites.
B.-S. Hua, M.-K. Tran, and S.-K. Yeung, “Pointwise convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 984–993
2018
Cited alongside, same era.
N. Verma, E. Boyer, and J. Verbeek, “Feastnet: Feature-steered graph convolutions for 3d shape analysis,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 2598–2606
2018
Cited alongside, same era.
C. Wang, B. Samari, and K. Siddiqi, “Local spectral graph convolution for point set feature learning,” in European conference on computer vision , 2018, pp. 52–66
2018
Cited alongside, same era.
J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in IEEE conference on computer vision and pattern recognition , 2018, pp. 9397–9406
2018
Cited alongside, same era.
F. Pistilli, G. Fracastoro, D. Valsesia, and E. Magli, “Learning graph-convolutional representations for point cloud denoising,” in European Conference on Computer Vision , 2020, pp. 103–118
2020
Later among the works it cites.
H. Gao, X. Zhu, S. Lin, and J. Dai, “Deformable kernels: Adapting effective receptive fields for object deformation,” in International Conference on Learning Representations , 2020, pp. 1–15
2020
Later among the works it cites.
Y. Chen, X. Dai, M. Liu, D. Chen, L. Yuan, and Z. Liu, “Dynamic convolution: Attention over convolution kernels,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 030–11 039
2020
Later among the works it cites.
S. Shan, Y. Li, and J. B. Oliva, “Meta-neighborhoods,” Advances in Neural Information Processing Systems , vol. 33, pp. 5047–5057, 2020
2020
Later among the works it cites.
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S. Wang, S. Suo, W.-C. Ma, A. Pokrovsky, and R. Urtasun, “Deep parametric continuous convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2589–2597
2018
Cited alongside, same era.
H. Thomas, F. Goulette, J.-E. Deschaud, B. Marcotegui, and Y. LeGall, “Semantic classification of 3d point clouds with multiscale spherical neighborhoods,” in International conference on 3D vision , 2018, pp. 390–398
2018
Cited alongside, same era.
X. Roynard, J.-E. Deschaud, and F. Goulette, “Classification of point cloud for road scene understanding with multiscale voxel deep network,” in 10th workshop on Planning, Perception and Navigation for Intelligent Vehicules , 2018, pp. 1–6
2018
Cited alongside, same era.
L. Landrieu and M. Simonovsky, “Large-scale point cloud semantic segmentation with superpoint graphs,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4558–4567
2018
Cited alongside, same era.
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert, “Pcn: Point completion network,” in 2018 International Conference on 3D Vision , 2018, pp. 728–737
2018
Cited alongside, same era.
C.-H. Lin, C. Kong, and S. Lucey, “Learning efficient point cloud generation for dense 3d object reconstruction,” in AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018, pp. 7114–7121
2018
Cited alongside, same era.
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng, “Ec-net: an edge-aware point set consolidation network,” in European Conference on Computer Vision , 2018, pp. 386–402
2018
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 , vol. 38, no. 5, pp. 1–12, 2019
2019
Cited alongside, same era.
N. Ma, X. Zhang, J. Huang, and J. Sun, “Weightnet: Revisiting the design space of weight networks,” in European Conference on Computer Vision , 2020, pp. 776–792
2020
Later among the works it cites.
X. Yan, C. Zheng, Z. Li, S. Wang, and S. Cui, “Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5589–5598
2020
Later among the works it cites.
A. Boulch, “Convpoint: Continuous convolutions for point cloud processing,” Computers & Graphics , vol. 88, pp. 24–34, 2020
2020
Later among the works it cites.
M. Liu, L. Sheng, S. Yang, J. Shao, and S.-M. Hu, “Morphing and sampling network for dense point cloud completion,” in AAAI conference on artificial intelligence , vol. 34, no. 07, 2020, pp. 11 596–11 603
2020
Later among the works it cites.
Z. Huang, Y. Yu, J. Xu, F. Ni, and X. Le, “Pf-net: Point fractal network for 3d point cloud completion,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 7662–7670
2020
Later among the works it cites.
X. Wang, Y. Xu, K. Xu, A. Tagliasacchi, B. Zhou, A. Mahdavi-Amiri, and H. Zhang, “Pie-net: Parametric inference of point cloud edges,” Advances in neural information processing systems , vol. 33, pp. 20 167–20 178, 2020
2020
Later among the works it cites.
Z. J. Yew and G. H. Lee, “Rpm-net: Robust point matching using learned features,” in IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 824–11 833
2020
Later among the works it cites.
M. Guo, J. Cai, Z. Liu, T. Mu, R. R. Martin, and S. Hu, “PCT: point cloud transformer,” Comput. Vis. Media , vol. 7, no. 2, pp. 187–199, 2021
2021
Later among the works it cites.
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 12, pp. 4338–4364, 2021
2021
Later among the works it cites.
H. Zhou, Y. Feng, M. Fang, M. Wei, J. Qin, and T. Lu, “Adaptive graph convolution for point cloud analysis,” in ICCV , 2021, pp. 4965–4974
2021
Later among the works it cites.
D. Zhang, X. Lu, H. Qin, and Y. He, “Pointfilter: Point cloud filtering via encoder-decoder modeling,” IEEE Transactions on Visualization and Computer Graphics , vol. 27, no. 3, pp. 2015–2027, 2021
2021
Later among the works it cites.
K. Fu, S. Liu, X. Luo, and M. Wang, “Robust point cloud registration framework based on deep graph matching,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8893–8902
2021
Later among the works it cites.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 259–16 268
2021
Later among the works it cites.
A. Goyal, H. Law, B. Liu, A. Newell, and J. Deng, “Revisiting point cloud shape classification with a simple and effective baseline,” in International Conference on Machine Learning , 2021, pp. 3809–3820
2021
Later among the works it cites.
M. Xu, J. Zhang, Z. Zhou, M. Xu, X. Qi, and Y. Qiao, “Learning geometry-disentangled representation for complementary understanding of 3d object point cloud,” in AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3056–3064
2021
Later among the works it cites.
M. Xu, R. Ding, H. Zhao, and X. Qi, “Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3173–3182
2021
Later among the works it cites.
T. Xiang, C. Zhang, Y. Song, J. Yu, and W. Cai, “Walk in the cloud: Learning curves for point clouds shape analysis,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 915–924
2021
Later among the works it cites.
S. Qiu, S. Anwar, and N. Barnes, “Geometric back-projection network for point cloud classification,” IEEE Transactions on Multimedia , vol. 24, pp. 1943–1955, 2021
2021
Later among the works it cites.
X. Ma, C. Qin, H. You, H. Ran, and Y. Fu, “Rethinking network design and local geometry in point cloud: A simple residual mlp framework,” in International Conference on Learning Representations , 2021, pp. 1–15
2021
Later among the works it cites.
H. Lei, N. Akhtar, and A. Mian, “Spherical kernel for efficient graph convolution on 3d point clouds,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 10, pp. 3664–3680, 2021
2021
Later among the works it cites.
D. Li, J. Hu, C. Wang, X. Li, Q. She, L. Zhu, T. Zhang, and Q. Chen, “Involution: Inverting the inherence of convolution for visual recognition,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 321–12 330
2021
Later among the works it cites.
I. Bello, “Lambdanetworks: Modeling long-range interactions without attention,” in International Conference on Learning Representations , 2021, pp. 1–14
2021
Later among the works it cites.
G. Qian, A. Abualshour, G. Li, A. K. Thabet, and B. Ghanem, “PU-GCN: point cloud upsampling using graph convolutional networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 683–11 692
2021
Later among the works it cites.
W. Feng, J. Li, H. Cai, X. Luo, and J. Zhang, “Neural points: Point cloud representation with neural fields for arbitrary upsampling,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 612–18 621
2022
Closest in time.
S. Qiu, S. Anwar, and N. Barnes, “Pu-transformer: Point cloud upsampling transformer,” in Proceedings of the Asian Conference on Computer Vision , 2022, pp. 2475–2493
2022
Closest in time.
H. Chen, Z. Wei, Q. Xie, M. Wei, and J. Wang, “Method for extracting multiple circle primitives extraction of aircraft surface based on 3d point cloud deep learning,” Journal of Mechanical Engineering , vol. 58, no. 14, pp. 190–202, 2022
2022
Closest in time.
M. Lin and A. Feragen, “diffconv: Analyzing irregular point clouds with an irregular view,” in European Conference on Computer Vision . Springer, 2022, pp. 380–397
2022
Closest in time.
H. Zhou, H. Chen, Y. Zhang, M. Wei, H. Xie, J. Wang, T. Lu, J. Qin, and X. Zhang, “Refine-net: Normal refinement neural network for noisy point clouds,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 1, pp. 946–963, 2023
2023
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” in Annual Conference on Neural Information Processing Systems , 2015, pp. 2017–2025
2025
Closest in time.