Scientific Visualization and Computer Graphics

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Class overview

The course covers the following topics:


  • aims and scope of data visualization
  • types of visualization (SciVis, InfoVis, SoftVis)
  • visualization vs computer graphics

The Visualization Pipeline

  • definition and steps
  • importance of mapping and inverse mapping
  • implementation issues

Data Representation

  • from continuous to discrete data and back
  • interpolation functions and techniques
  • grid types (uniform, structured, rectilinear, unstructured)
  • cell types (1D, 2D, 3D)
  • supersampling, resampling, computing derivatives

Scalar visualization

  • color mapping and colormap design issues
  • height and warp plots
  • contouring (2D and 3D)
  • slicing

Vector visualization

  • directional color coding
  • vector glyphs
  • streamlines and related stream objects
  • vector field decomposition

Tensor visualization

  • tensor definition
  • principal component analysis
  • per-component visualization
  • anisotropy visualization
  • tensor glyphs
  • hyperstreamlines

Volume visualization

  • relation to scalar visualization
  • object-order volume rendering
  • image-order volume rendering
  • transfer functions design

Dataset processing algorithms

  • scattered point interpolation
  • triangulation
  • grid segmentation