Course Syllabus


This course covers selected topics in modern computer graphics research. Specific subjects include stochastic sampling and patterns, photorealistic image synthesis, and machine learning in computer graphics. 3 credits


This is a graduate-level class for computer science students. Undergraduates and students from other departments may enroll with instructor permission.


  • Lecture time: Tue&Thu 2:30-3:45pm, at LGRC A310
  • Instructor: Rui Wang (ruiwang [at]
    • Office hour: Tue&Thu 4:00-5:00pm.
  • Class email: cmpsci-691av-01-spr12 [at]
  • Moodle course link:
    • We will use moodle for assignment submission, class discussion forum, and quizzes.
    • Slides are posted on this website (see sidebar on the left) and not in moodle.


  • There is no required textbook for this class. Below are a few recommended books:
    • The Fourier Transform and Its Applications (2nd edition), by Ronald N. Bracewell (Amazon link)
    • Principles of Digital Image Synthesis (2nd edition), by Andrew S. Glassner (Amazon link)
    • Physically-Based Rendering (2nd edition), by Matt Pharr and Greg Humphreys (Amazon link)
    • Spectral Graph Theory, by Fan Chuang (Amazon link)


  • 3 assignments, 1 mid-term (take-home), 1 final project, and various quizzes.
  • There is no final exam, instead, there is 1 final project.
  • Grading: 45% assignments, 20% mid-term, 25% final project, 10% quiz.

Schedule and Slides:

Resources and Links: