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The efficient treatment of long-range interactions for point clouds is a challenging problem in many scientific machine learning applications.
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Wavelet scattering regression of quantum chemical energies
M. Hirn, S. Mallat, and N. Poilvert · 2017
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3DContextNet: K-d tree guided hierarchical learning of point clouds using local and global contextual cues
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Y. Zhou and O. Tuzel · 2018
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A. H. Barnett, J. Magland, and L. af Klinteberg · 2019
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Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models
R. Klokov and V. Lempitsky · 2017
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Pointnet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Octnet: Learning deep 3D representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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Deep sets
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Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
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PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors
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An electrostatic spectral neighbor analysis potential for lithium nitride
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Relation-shape convolutional neural network for point cloud analysis
Y. Liu, B. Fan, S. Xiang, and C. Pan · 2019
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Graph attention convolution for point cloud semantic segmentation
L. Wang, Y. Huang, Y. Hou, S. Zhang, and J. Shan · 2019
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MSGCNN: Multi-scale graph convolutional neural network for point cloud segmentation
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Deep neural network for the dielectric response of insulators
L. Zhang, M. Chen, X. Wu, H. Wang, W. E, and R. Car · 2019
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Multi-scale approach for the prediction of atomic scale properties
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Multipole graph neural operator for parametric partial differential equations
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https://github.com/Forgotten/Efficient_Long-Range_Convolutions_for_Point_Clouds, 2020
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Multi-scale dynamic graph convolution network for point clouds classification
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