SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

CVPR 2018 Matthias Fey • Jan Eric Lenssen • Frank Weichert • Heinrich Müller

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. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights.

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