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Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

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papermodel
Créateur:
ThoSmiKeaYan
Hôte:avatar
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. Our network uses filters built from spherical harmonics; due to the mathematical consequences of this filter choice, each layer accepts as input (and guarantees as output) scalars, vectors, and higher-order tensors, in the geometric sense of these terms. We demonstrate the capabilities of tensor field networks with tasks in geometry, physics, and chemistry. changes for NIPS submission

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arxiv.org

Tasks

computer vision

Tags

Machine LearningArtificial IntelligenceComputer Vision and Pattern RecognitionNeural and Evolutionary Computing

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