Introduction to Scientific Visualization

Special Graduate Course

Autumn 2006

The art of visualization centers around helping people explore or explain data through software systems that provide static or interactive visual representations. Visualization designers exploit the high bandwidth channel of human visual perception to allow people to comprehend information orders of magnitude more quickly than they could through reading raw numbers or text. Visualization is useful for detecting patterns, assessing situations, sharing data, and steering simulation. Understanding, and, ultimately, knowledge cannot be delivered directly from computation. Visualization is the tool through which computation presents a face to an end user and, by so doing, is allows the user to derive knowledge from data.


During the past 20 years the world has experienced an “information big bang”, an exponential explosion of data. New information produced in the two years since 2003 exceeds the information contained in all previously created documents. Of all this new information produced since 2003, more than 90% takes digital form, vastly exceeding information produced in paper and film forms. But raw information is by itself of questionable value. We are continually challenged to make sense of the enormous growth and onslaught of information and use it in effective and efficient ways.


Among the greatest scientific challenges of the 21st century, will be to effectively understand and make use of the vast amount of information being produced. Our primary problem is no longer acquiring sufficient information, but rather making use of it and sharing it in a collaborative fashion. If we are to use information to make discoveries in science, engineering, medicine, art, and the humanities, we must create new theories, techniques, and methods for its management and analysis. By its very nature, visualization addresses the challenges created by such excess – too many data points, too many variables, too many time steps, and too many potential explanations. Thus, as we work to tame the accelerating information explosion and employ it to advance scientific, biomedical, and engineering research, visualization will be among our most important tools. This course aims at introducing to scientist, engineers, as well as practitioners in medicine the basic fundamentals of data visualization. During the course, we will focus on the following topics:


Basic knowledge of computer graphics and parallel programming is an asset.


There is no specific textbook for this course. We will provide notes and scientific papers.

Several downloadable documents from Kitware that describe VTK, Paraview, and VolView will be used as part of teaching the use of a visualization toolkit. These include the Paraview user's guide, and the VolView user's guide. A description on how to get Paraview, VolView, and VTK can be found here.


The students will be evaluated based on three criteria:


  1. Class Participation: Class participation will count for 10% of the final mark. This will include presenting papers in class and to work on small assignments performed during class hours.


  1. Project: The project is where the students get really evaluated; it will count for 60% of the total score. It has to be a significant peace of work that needs to be approved by the instructor. Project scope can range from implementing algorithms presented in scientific publications or new ones as long as the scope of the work does not exceed the time limit of the session.


  1. Project Report and Presentation: Each student will have to write a final report on their project in the form of a ten pages paper in IEEE double columns format. In addition, the student will have to make a 20 minutes presentation of their work during a special workshop that will be organized during the term. This will count for 30% of the final mark.






All students must complete a term project. Students must work individually. The project consist of the various aspect of sensor based 3D modeling using photo-grammetry and range sensors.  The results on the projects are to be demonstrated near the end of the term as part of a presentation to the class. A final paper is also required. Eac


h student must complete a term project.   Details on the projects can be found on the web. The results on the projects are required to be presented near the end of the term. Completion of the project is a requirement for the course. Failure to submit your project before the last day of classes will result in a failing grade. In addition, a demo must be given at a scheduled time.


The proportion of the final mark associated with the different components of the course are as follows:

A graduate student who fails to submit the project on the last day of this class will receive a failing grade. 


Multiple View Geometry in Computer Vision

by Richard Hartley and Andrew Zisserman

     Cambridge University Press





Alternative Book:

The Geometry from Multiple Images


by Olivier Faugeras and Quan-Tuan Luong


MIT Press



Note: The lectures do not follow any textbooks.

Other resources:

Course website

Good Tutorial:


Dr. Boulanger graduated from Laval University in Engineering Physics. He also received his Masters in Physics from the same university and his Ph.D. in Electrical Engineering from the University of Montreal. He worked for 18 years at the National Research Council of Canada as a senior research officer where his primary research interest were in 3D computer vision, rapid product development, and virtualized reality systems.  Since July 1st 2001, he is working as a professor at the Department of Computing Science at the University of Alberta doing research and teaching on virtualized reality systems. He is also an adjunct scientist and principal investigator for new media at TRLabs and at the Banff Centre. In 2004, Dr. Boulanger was awarded an iCORE industrial chair in Collaborative Virtual Environment.

 He has published more than 150 scientific papers in various Journals and Conferences. He is on the editorial board of two major academic journals. Dr. Boulanger is also on many international committees and frequently gives lectures on rapid product development and virtualized reality. He is the founder of the Canadian Virtualized Reality Systems Working Group. He is also the Director of the Advanced Man Machine Interface Laboratory. On the commercial side, Dr Boulanger is the president of PROTEUS Consulting Inc., an Alberta-based consulting firm specialized in Virtual Reality Applications.

Course Material:

The course material includes notes posted on the web and additional assigned reading. A detailed list of topics covered in assignments will be published as the course progresses.

Related Links

NASA list of visualization sites. Please consult a Gallery of Data Visualization web page by with Tuftian-like commentary on the visual presentation of information.

Visualization Toolkits

The Visualization Toolkit (VTK).

Advanced Visual Systems (AVS) home page. Also, the International AVS Centre, which has tutorials and downloadable AVS modules as well as other information.

Bill Hibbard's VisAD and Vis5D packages.

OpenDX (data explorer) home page.

Other Visualization Courses

A portion of this course was taught at SIGGAPH 2003 by Russ Taylor, Colin Ware, and Victoria Interrante as course #45, "Perceptually Based Visualization Design."

The ACM SIGGRAPH Education for Visualization Committee produced a draft report on a curriculum for visualization education.

UNC Information and Library Science INLS 110-99, "Selected Topics: Information Visualization," taught by Rolf Daessler (visiting from Potsdam, Germany) in the summer of 2001.

Utah CS5630, "Scientific Visualization," taught by Chris Johnson in the spring of 2002.

University of Maryland Baltimore County CSMC491B/691B, "Visualization Techniques," taught by David Ebert and Penny Rheingans in the autumn of 1999.

University of New Hampshire OE/CS 867:767, "Interactive Data Visualization," taught by Colin Ware.

Virginia Tech ESM4714, "Scientific Visual Analysis with Multimedia," taught by Ron Kriz in the spring of 2000.

UC Davis ECS 177, "Introduction to Visualization," taught by Ken Joy in the winter of 1999.

FSU CIS4930/5930, "Visualization," taught by David Banks in 2000.

RIT CSC 472/672, "Data Visualization," taught by G. Scott Owen in the fall of 1996.

CS 237, "Interdisciplinary Scientific Visualization," taught by David Laidlaw and Daniel Acevedo in the fall of 2003 and in prior years by David Laidlaw, is a project-based course that teams computer scientists with domain scientists to "fund," design, implement, and evaluate a project in scientific visualization.

The San Diego State University offered "Scientific Visualization" as CS 689 in the fall of 2000.

Two courses on image processing for volume graphics, transfer functions, and level sets can be found at the National Library of Medicine site.

Visualization Groups and Sites

Lawrence Livermore National Laboratories. Los Alamos National Laboratories. Lawrence Berkeley National Laboratories. NASA Ames.