Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes. (arXiv:2205.02904v1 [cs.GR])

Web: http://arxiv.org/abs/2205.02904

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. The framework is based on reducing
the neural aspect to a prediction of a matrix for a single given point,
conditioned on a global shape descriptor. The field of matrices is …

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