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We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations.
Clustering to minimize the maximum intercluster distance
Gonzalez, T.F.: · 1985
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Wavelets on graphs via spectral graph theory
Hammond, D.K., Vandergheynst, P., Gribonval, R.: · 2011
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: · 2014
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3D Shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 2015
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., Vandergheynst, P.: · 2016
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PointNet: Deep learning on point sets for 3D classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M.: · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Simonovsky, M., Komodakis, N.: · 2017
Cited alongside, same era.
Local spectral graph convolution for point set feature learning
Wang, C., Samari, B., Siddiqi, K.: · 2018
Cited alongside, same era.
Point convolutional neural networks by extension operators
Atzmon, M., Maron, H., Lipman, Y.: · 2018
Cited alongside, same era.
SpiderCNN: Deep learning on point sets with parameterized convolutional filters
Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: · 2018
Cited alongside, same era.
A graph-CNN for 3D point cloud classification
Zhang, Y., Rabbat, M.: · 2018
Cited alongside, same era.
PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors
Deng, H., Birdal, T., Ilic, S.: · 2018
Cited alongside, same era.
SO-Net: self-organizing network for point cloud analysis
Li, J., Chen, B.M., Lee, G.H.: · 2018
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Dynamic graph CNN for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: · 2019
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Deformable filter convolution for point cloud reasoning
Xiong, Y., Ren, M., Liao, R., Wong, K., Urtasun, R.: · 2019
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KPConv: Flexible and deformable convolution for point clouds
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: · 2019
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PointConv: Deep convolutional networks on 3D point clouds
Wu, W., Qi, Z., Fuxin, L.: · 2019
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ClusterNet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis
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PPFNet: Global context aware local features for robust 3D point matching
Deng, H., Birdal, T., Ilic, S.: · 2018
Cited alongside, same era.
Spherical CNNs
Cohen, T.S., Geiger, M., Köhler, J., Welling, M.: · 2018
Cited alongside, same era.
Learning SO(3) equivariant representations with spherical CNNs
Esteves, C., Allen-Blanchette, C., Makadia, A., Daniilidis, K.: · 2018
Cited alongside, same era.
Chen, C., Li, G., Xu, R., Chen, T., Wang, M., Lin, L.: · 2019
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Rotation invariant convolutions for 3D point clouds deep learning
Zhang, Z., Hua, B.S., Rosen, D.W., Yeung, S.K.: · 2019
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Spherical fractal convolutional neural networks for point cloud recognition
Rao, Y., Lu, J., Zhou, J.: · 2019
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DeepGCNs: Can GCNs go as deep as CNNs?
Li, G., Müller, M., Thabet, A., Ghanem, B.: · 2019
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