A Generative Model for Volume Rendering

Matthew Berger, Jixian Li, Joshua A. Levine

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.

Original languageEnglish (US)
Article number8316963
Pages (from-to)1636-1650
Number of pages15
JournalIEEE Transactions on Visualization and Computer Graphics
Volume25
Issue number4
DOIs
StatePublished - Apr 1 2019

Keywords

  • deep learning
  • generative adversarial networks
  • generative models
  • Volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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