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Found 3 papers, 2 papers with code
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
Neural Computed Tomography
Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views.
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
Applications like rendering, simulations and 3D printing require meshes to be manifold so that they can interact with the world like the real objects they represent.
