A Practical Approach to Spectral Volume Rendering

  • Authors:
  • Steven Bergner;Torsten Moller;Melanie Tory;Mark S. Drew

  • Affiliations:
  • School of Computing, Science, Simon Fraser University, Burnaby BC, V5A 1S6 Canada;School of Computing, Science, Simon Fraser University, Burnaby BC, V5A 1S6 Canada;Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver BC, V6T 1Z4 Canada.;School of Computing, Science, Simon Fraser University, Burnaby BC, V5A 1S6 Canada

  • Venue:
  • IEEE Transactions on Visualization and Computer Graphics
  • Year:
  • 2005

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Abstract

To make a spectral representation of color practicable for volume rendering, a new low-dimensional subspace method is used to act as the carrier of spectral information. With that model, spectral light material interaction can be integrated into existing volume rendering methods at almost no penalty. In addition, slow rendering methods can profit from the new technique of postillumination驴generating spectral images in real-time for arbitrary light spectra under a fixed viewpoint. Thus, the capability of spectral rendering to create distinct impressions of a scene under different lighting conditions is established as a method of real-time interaction. Although we use an achromatic opacity in our rendering, we show how spectral rendering permits different data set features to be emphasized or hidden as long as they have not been entirely obscured. The use of postillumination is an order of magnitude faster than changing the transfer function and repeating the projection step. To put the user in control of the spectral visualization, we devise a new widget, a "light-dial,驴 for interactively changing the illumination and include a usability study of this new light space exploration tool. Applied to spectral transfer functions, different lights bring out or hide specific qualities of the data. In conjunction with postillumination, this provides a new means for preparing data for visualization and forms a new degree of freedom for guided exploration of volumetric data sets.