Learning from High-Resolution Meshes

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Learning from High-Resolution Meshes

HodgeNet: Learning Spectral Geometry on Triangle Meshes

We present a technique for learning from meshes built from standard geometry processing modules and operations.

We show that low-order eigenvalue/eigenvector computation from operators parameterized using discrete exterior calculus is amenable to efficient approximate backpropagation, yielding spectral per-element or per-mesh features with similar formulas to classical descriptors like the heat/wave kernel signatures.

Our model uses few parameters, generalizes to high-resolution meshes, and exhibits performance and time complexity on parwith past work.

Authors

Dmitriy Smirnov, Justin Solomon

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