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

May 9, 2022, 1:11 a.m. | Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, Thibault Groueix

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv learning neural

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