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Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks.
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3d shapenets: A deep representation for volumetric shapes
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Learning shape correspondence with anisotropic convolutional neural networks
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Convolutional neural networks on graphs with fast localized spectral filtering
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Deep residual learning for image recognition
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Geometric deep learning: going beyond euclidean data
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Submanifold sparse convolutional networks
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Geometric deep learning on graphs and manifolds using mixture model cnns
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
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3d graph neural networks for rgbd semantic segmentation
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Octnet: Learning deep 3d representations at high resolutions
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Semi-supervised classification with graph convolutional networks
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Pointnet: Deep learning on point sets for 3d classification and segmentation
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Volumetric and multi-view cnns for object classification on 3d data
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
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Segcloud: Semantic segmentation of 3d point clouds
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
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