[47] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.
[48] Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, and Yun Fu. Rethinking network design and local geometry in point cloud: A simple residual mlp framework. arXiv preprint arXiv:2202.07123, 2022.
[49] Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi. Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3173-3182, 2021.
[50] Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. Point transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 16259-16268, 2021.
[51] Cheng Zhang, Haocheng Wan, Xinyi Shen, and Zizhao Wu. Pvt: Point-voxel transformer for point cloud learning. International Journal of Intelligent Systems, 37(12):11985–12008, 2022.
[52] Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, and Chunjing Xu. Voxel transformer for 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3164-3173, 2021.
[53] Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu. Pct: Point cloud transformer. Computational Visual Media, 7:187-199, 2021.
[54] Abien Fred Agarap. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375, 2018.
[55] Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289, 2015.
[56] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016.
[57] Diganta Misra. Mish: A self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681, 2019.
[58] Jonathan T Barron. Continuously differentiable exponential linear units. arXiv preprint arXiv:1704.07483, 2017.
[59] Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Pointcnn: Convolution on x-transformed points. arXiv preprint arXiv:1801.07791, 2018.
[60] Tristan Bereau, Denis Andrienko, and O. Anatole Von Lilienfeld. Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules. J. Chem. Theory Comput., 11(7):3225-3233, July 2015.
[61] Chungwen Liang, Gabriele Tocci, David M. Wilkins, Andrea Grisafi, Sylvie Roke, and Michele Ceriotti. Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water. Phys. Rev. B, 96(4):041407, July 2017.
[62] Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Phys. Rev. Lett., 120(14):143001, April 2018.
[63] Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Computer Physics Communications, 228:178-184, July 2018.
[64] Vitaliy Kurlin. Exactly computable and continuous metrics on isometry classes of finite and 1-periodic sequences. arXiv preprint arXiv:2205.04388, 2022.
[65] Vitaliy Kurlin. Computable complete invariants for finite clouds of unlabeled points under euclidean isometry. arXiv preprint arXiv:2207.08502, 2022.