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      "statement": "# Tensor Field Networks for equivariant 3D point-cloud processing\n\nBuilds rotation-equivariant point convolutions using spherical-harmonic filters and Clebsch–Gordan tensor products, propagating scalar, vector and higher-order geometric features.\n\n## Scientific scope\n\nThis assessment concerns the equivariant architecture and its evaluated 3D/physics tasks. Seven original authors; Thomas and Smidt share contribution. Representation theory and tensor products are inherited mathematics; SO(3) construction is not an automatic O(3), physical-correctness or conservation guarantee. Distinct from the later e3nn software framework.\n\n## People and systems\n\nNathaniel Thomas; Tess Smidt; Steven Kearnes; Lusann Yang; Li Li; Kai Kohlhoff; Patrick Riley\n\n\n\n## Original sources\n\n- [Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds](https://arxiv.org/pdf/1802.08219v1)\n",
      "summary": "Builds rotation-equivariant point convolutions using spherical-harmonic filters and Clebsch–Gordan tensor products, propagating scalar, vector and higher-order geometric features.",
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