[Archived] CS190I: Introduction to Offline Rendering

Fall 2022

Course Description
  [Path tracing (Big Hero 6 by Disney)]
  [Participating media (Novak et al.)]
[Offline Denoising (KPCN) (Bako et al.)]
[Disney Principled BRDF (Burley et al.)]    

This course will teach you everything about offline rendering, so you will be able to write a fully functional industry-level renderer (such as Disney's Hyperion and Pixar's RenderMan) that produces stunning graphics. Topics in this course will cover the physics of light, the rendering equation, Monte Carlo integration, path tracing, physically-based reflectance models, participating media, other advanced light transport methods, production rendering approaches, and so on.

This course is the step stone if you want to find a job in the animation companies and/or if you want to apply for graduate school focusing on Computer Graphics research. Like CS180, our CS190I will not be very easy, but will be both interesting and rewarding. Graphics is AWESOME.

Course Comparison

Some of you (especially those who have taken CS180) may wonder what the differences are between CS180 and CS190I. The answer is that CS180 to CS190I is like Artificial Intelligence to Deep Learning. CS180 is an overview in Computer Graphics, but CS190I is focused on accurate and high-quality offline rendering within the framework of ray tracing. Prior knowledge in CS180 is PREFERRED but NOT STRICTLY REQUIRED.

The difference between CS291A and CS190I is much more clear. CS291A is focused on real-time (>30 FPS) approaches. They are smart, fast but less accurate, usually used in video games and VR/AR applications. CS190I cares more about correctness and quality, but does not exploit GPU computing and does not worry about slow performance, thus is more used in animations, etc.

Also note that, this course is listed in the category "Intelligent and Interactive Systems". But it is to build up our intelligence for the design of interactive systems. Only a small part of the topics from this course is related to Machine Learning / Deep Learning.

Important Note

This course has been archived. Please visit Prof. Lingqi Yan's website for its latest version.