“Redundancy Visualisation”

This was a wonderful little term mentioned in passing at a synchrotron vis meeting. It sort of means what can be thrown away and still produce the message or story that the visualisation wishes to convey.

There are two areas where data can be thrown away – from the original or derived data set so you select parts that are appropriate; or from the items you wish to show in the visualisation itself – cropping isosurfaces or streamlines for example.

A tomography pipeline operation should be mentioned that addresses the 100GB problem – and could be said to be the first half.

100GB Problem

I have a scanned 3D data set that is about 4k x 4k x 4k in size, with 16 bits per voxel grey scale we have, 128 GB or raw data. How do we visualise this.

We can just use lots of CPUs and GPUs and this is fine – although not necessarily straightforward. See video from (TO Upload)

Do a simple dataflow so steps:

  • Load the complete data set into a fat memory workstation – you have to find one of these but there are ‘many’ 1/2 TB RAM systems out there.
  • Volume visualise the complete data set that works on simple GPU parallel code.
  • Select volume of interest
  • Crop this volume – aiming for about 1-2 GB
  • Extract this sub-volume and then possibly scale to 8 bits per volxel
  • You have a data volume about 1/2 – 1 GB that can go into your laptop for normal visualisation and hand editing / markup. Simple.

Not always practical but then there are lots of cool code that only works on <2GB volume due to meshing , level-set analysis and your heart and CPU are freed.

Important to go back to the raw volume and check you are have the right conclusions.


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