Scientific Visualization (CG-463)

This course introduces and details various algorithms for visualization of data. For this course, a focused approach will be taken by using the VTK library. Both core and state-of-the art visualization algorithms will be covered.
This course details visualization technologies and its applications in scientific and medical research. Object-Oriented Programming with the Visualization Toolkit is learnt through experiments during the course. Upon the completion of the course, the student should be able to understand and develop an effective visualization pipeline to carry out a desired visualization function. Specifically,he/she would be able to define the topology, geometry and attribute data of a dataset for the visualization tasks, apply the Source, Filter and Mapper process objects into data acquisition, transformation and graphic representation, and analyze and evaluate the visualization results.

Tentative Course Outline:

  • Introduction to Scientific Visualization
  • Introduction to 3D Computer Graphics
  • Introduction to the physical description of rendering, image-order and object-order
  • Difference between surface vs volume rendering
  • Different surface properties (ambient, diffuse, secular)
  • Geometric transformations
  • Volume rendering
  • Volume classification
  • Volume segmentation
  • Volume illumination
  • Visualisation Pipeline: visualisation model, process objects
  • Explicit and implicit Models
  • Graphical Data Representation
  • Polygonal data, structured points and grid, rectilinear grid, unstructured points and grid
  • Interpolation functions and coordinate transformation
  • Topological operations
  • Visualisation algorithms: scalar, vector, tensor and modelling algorithms
  • Marching Cubes
  • Implicit functions
  • Visualization Applications: 3D medical imaging
  • Recent advances
  • Conclusion
Total Lectures: 40
Total Assignments: 5-7
Total Quizzes: 5-7
Reference Books:
Projects List (including but not limited to):
  1. Volume visualizer for biomedical dataset
  2. Tensor visualizer
  3. Surface extractor
  4. Volume deformer
  5. Flow visualizer
  6. Glyph visualizer