Photo-realistic rendering (PBR) is the task of using computers for producing synthetic images of digital scenes that are indistinguishable from what a real photo of that same scene would look like. It is a very challenging task which requires high quality geometric models of the objects in the scene, defining and assigning realistic materials for each of those objects, and a physically-based light propagation simulation.

The realistic simulation of light propagation is a key factor for producing photo-realistic images, since it allows computing the exact amount of light that would arrive to the camera film after multiple bounces and interactions with the scene objects, hence realistically reproducing a virtual photo of the scene. However, such a process is very computationally demanding, and usually requires a large amount of time and resources for producing a single image (recall that, for a movie, at least 25 images per second are required, in general).

PBR has a large set of applications, ranging from movies and computer games industry to more serious applications such as flight simulators. Perhaps the most typical example of the importance of this technology is architecture, where architects can interact with a realistic visualization of a building yet to be constructed. This allows them to eventually adapt the building to their own purposes taking into account the chosen materials and illumination considerations. Other important applications can be found in the car industry, plane industry, etc. The impact of this technology in the society is quite relevant, since it can help companies save millions of euros in expensive real prototypes by resorting to realistic digital simulations instead.

The goal of this project is to develop innovative methods for accelerating the synthesis of photo-realistic images. To this end, we resort to machine learning-based techniques, with a particular focus on a relatively recent approach called Bayesian Monte Carlo. These techniques allow learning from the synthesis of previous images and re-use the learned information to more efficiently compute new photo-realistic images. However, due to their complexity, a direct application of these techniques to PBR is cumbersome. This project has the general objective of making such application feasible.