Monte Carlo integration is a fantastic tool, but it’s not necessarily efficient if we don’t do it right! Solving the rendering equation requires a lot of computational resources, we better use our math kung-fu to better squeeze every drop of performance from the renderer. By drawing samples from our function with a probability proportional to their function value (importance sampling), we can substantially improve our convergence speed.
About the course:
This course aims to give an overview of basic and state-of-the-art methods of rendering. Offline methods such as ray and path tracing, photon mapping and many other algorithms are introduced and various refinement are explained.
The basics of the involved physics, such as geometric optics, surface and media interaction with light and camera models are outlined.
The apparatus of Monte Carlo methods is introduced which is heavily used in several algorithms and its refinement in the form of stratified sampling and the Metropolis-Hastings method is explained.
At the end of the course students should be familiar with common techniques in rendering and find their way around the current state-of-the-art of the field. Furthermore the exercises should deepen the attendees’ understanding of the basic principles of light transport and enable them to write a simple rendering program themselves.
These videos are the recordings of the lectures of 2015 at the Teschnische Universität Wien by Károly Zsolnai and Thomas Auzinger
Course website and slides →
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