横跨了二维投影法和体素法的一篇 Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas. 2016. Volumetric and multi-view CNNs for object classification on 3d data. In Computer Vision and Pattern Recognition (CVPR). 5648âĂŞ5656.
基于体素的变分自动编码器来解决形状重构 Andrew Brock, Theodore Lim, J.M. Ritchie, and Nick Weston. 2016. Generative and Discriminative Voxel Modeling with Convolutional Neural Networks. In NIPS 3D Deep LearningWorkshop.
同作者的基于体素法的形状配准 Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or. 2018. ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning. ACMTrans. Graph. 38, 1, Article 1 (Dec. 2018), 14 pages. https://doi.org/ 10.1145/3267347
用神经网络对局部连接区域进行处理 Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning Convolutional Neural Networks for Graphs. In International Conference on Machine Learning (ICML).
提出了基于扩散的卷积法 James Atwood and Don Towsley. 2016. Diffusion-convolutional Neural Networks. In Proceedings ofthe 30th International Conference on Neural Information Processing Systems (NIPS’16). Curran Associates Inc., USA, 2001–2009. http://dl.acm.org/ citation.cfm?id=3157096.3157320
将表面参数化为局部小批块 Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs. In Proc. CVPR, Vol. 1. 3.
在语义分割问题上使用了有向卷积 Haotian Xu, Ming Dong, and Zichun Zhong. 2017. Directionally Convolutional Networks for 3D Shape Segmentation. In Proceedings ofthe IEEE International Conference on Computer Vision. 2698–2707.
使用拉普拉斯图表示法来生成三维形状 Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, and Burna Joan. 2018. Surface Networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
提出了图的顶点滤波法但没有结合池化操作 F. P. Such, S. Sah, M. A. Dominguez, S. Pillai, C. Zhang, A. Michael, N. D. Cahill, and R. Ptucha. 2017. Robust Spatial Filtering With Graph Convolutional Neural Networks. IEEE Journal ofSelected Topics in Signal Processing 11, 6 (Sept 2017), 884–896.
介绍如何对局部特征进行深度学习的先驱 Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst. 2015. Geodesic convolutional neural networks on riemannian manifolds. In Proceedings ofthe IEEE international conference on computer vision workshops. 37–45.
一篇关于对几何进行深度学习的全面的综述 Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34, 4 (2017), 18–42. https://doi.org/10.1109/MSP.2017.2693418
将点云的点邻近信息利用起来并通过他们在特征空间中的相似性动态计算更新 Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2018a. Dynamic graph CNN for learning on point clouds. arXiv preprint arXiv:1801.07829 (2018).
定义了一种有效的利用点云体素化来对点云进行卷积的方法 Matan Atzmon, Haggai Maron, and Yaron Lipman. 2018. Point Convolutional Neural Networks by Extension Operators. ACMTrans. Graph. 37, 4 (July 2018), 71:1–71:12.
提出了PointCNN扩张了局部点云卷积的概念 Yangyan Li, Rui Bu, Mingchao Sun, and Baoquan Chen. 2018. PointCNN. CoRR abs/1801.07791 (2018).