  Constrained by the limitations of learning toolkits engineered for other
applications, such as those in image processing, many mesh-based learning
algorithms employ data flows that would be atypical from the perspective of
conventional geometry processing. As an alternative, 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 par
with past work.
