Compressive Neural Representations of Volumetric Scalar Fields

Y. Lu, K. Jiang, J. A. Levine, M. Berger

Research output: Contribution to journalArticlepeer-review

Abstract

We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time-varying scalar fields, optimizing to preserve spatial gradients, and random-access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.

Original languageEnglish (US)
Pages (from-to)135-146
Number of pages12
JournalComputer Graphics Forum
Volume40
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • CCS Concepts
  • • Computing methodologies → Neural networks; Image compression
  • • Human-centered computing → Visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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