Affiliation:
1. Massachusetts Institute of Technology
Abstract
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.
Funder
MIT?IBM Watson AI Laboratory
Adobe Systems
National Science Foundation Graduate Research Fellowship
National Science Foundation
CSAILSystems that Learn
Toyota?CSAIL Joint Research Center
Army Research Office
Air Force Office of Scientific Research
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
Cited by
20 articles.
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