Geometry encoding for numerical simulationsDownload PDF

Mar 08, 2021 (edited Apr 16, 2021)GTRL 2021 SpotlightReaders: Everyone
  • Keywords: Geometry encoding, Implicit representation, Neural network
  • TL;DR: We present a notion of geometry encoding suitable for machine learning-based numerical simulations
  • Abstract: We present a notion of geometry encoding suitable for machine learning-based numerical simulation. In particular, we delineate how this notion of encoding is different than other encoding algorithms commonly used in other disciplines such as computer vision and computer graphics. We also present a model comprised of multiple neural networks including a processor, a compressor and an evaluator. These parts each satisfy a particular requirement of our encoding. We compare our encoding model with the analogous models in the literature.
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