Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

4 results

  • Tutorial on the Visualization of Volumetric Data Using tomviz

  • Barnaby D.A. Levin, Yi Jiang, Elliot Padgett, Shawn Waldon, Cory Quammen, Chris Harris, Utkarsh Ayachit, Marcus Hanwell, Peter Ercius, David A. Muller, Robert Hovden
  • Journal:
    / Volume 26 / Issue 1 / January 2018
    Published online by Cambridge University Press:
    05 January 2018, pp. 12-17
    Print publication:
    January 2018
    • Article
      • You have access
    • PDF
    • HTML
    • Export citation

  • Tomography produces complex volumetric datasets containing the entire internal structure and density of an object in three dimensions (3D). Interpreting volumetric data requires 3D visualization but needs specialized software distinguishable from more familiar tools used in animation for 3D surface data. This tutorial reviews 3D visualization techniques for volumetric data using the open-source tomviz software package. A suite of tools including two-dimensional (2D) slices, surface contours, and full volume rendering provide quantitative and qualitative analysis of volumetric information. The principles outlined here are applicable to a wide range of 3D tomography techniques and can be applied to volumetric datasets beyond materials characterization.