ImprovingMeshQualityOfMarchingCubes

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The paper

Carlos Dietrich, Joao Comba, Luciana Nedel, Carlos Scheidegger, John Schreiner, Claudio Silva. Edge Transformations for Improving Mesh Quality of Marching Cubes. 2007, submitted.

Reproducing the results

The results in this paper can be completely reproduced using open-source software and publicly available datasets.

Alternative #1: Ubuntu repository

If you run Ubuntu Linux, you can simply add our APT repository

deb http://vgc.sci.utah.edu/ubuntu edgy main

and install the package "ectmc" we are currently creating. This will install all the software you need with the right versions, so running the experiments will be a matter of downloading the actual vistrail containing the workflows and running it:

# apt-get install ectmc

Follow these instructions.

Alternative #2: Downloading and using VisTrails manually

You can download all the individual software manually.

Our algorithm

ZIP File

Gzipped tarball

Core requirements

Teem

Trimesh2

Boost

Metro

Tetgen

Requirements for running the experiments

  • VTK Version 5.0 or higher, with Python bindings
  • [1] Matplotlib
  • [2] Afront

VisTrails packages

tarball of all necessary VisTrails packages.. Extract this file into your .vistrails/userpackages directory. This will allow VisTrails to see the packages with extended functionality. Then, add the following lines to your .vistrails/startup.py:

addPackage('ectmc')
addPackage('trimesh2')
addPackage('meshutils')
addPackage('afront')
addPackage('metro')
addPackage('teem')

If any of the software you installed is not in the PATH for the user that's running VisTrails, you can explicitly add the path as an extra parameter to these addPackages. For example:

addPackage('afront', '/home/user/downloads/afront')

or

addPackage('trimesh2', '/home/user/local/bin')

Datasets

The datasets we used are all available on the Volvis website. We use the NRRD toolset to access the data, so you need the NRRD header files to go with the raw volumes. You can get the NRRD headers and dataset we used here.

Vistrail

The vistrail you will need to open is available here. Each version shows a different technique. The topmost one is the one we are proposing (MACET).