Metropolis Light Transport is a powerful technique that can outperform the convergence speed of Bidirectional Path Tracing on most difficult scenes (what makes a scene difficult is a story on its own). It promises optimal importance sampling “along multiple steps” in the stationary distribution of the Markov chain. This means that it gets better and better over time in seeking the brighter parts of the path space, therefore caustics pop up converged in almost every case. The two earliest and most well-known variants are the Veach and Kelemen-style Metropolis Light Transport (also called Primary Sample Space MLT).
I will add that despite considering MLT as an unbiased algorithm, it suffers from an effect that we call start-up bias.
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
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