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We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes.
Spectral Graph Theory
F. R. K. Chung · 1997
Earlier work this paper cites.
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L. Piegl and W. Tiller · 1997
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Earlier work this paper cites.
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Earlier work this paper cites.
Intrinsic shape context descriptors for deformable shapes
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Earlier work this paper cites.
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Earlier work this paper cites.
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