Global illumination and filtering on the GPU

OData support
Supervisor:
Dr. Szirmay-Kalos László
Department of Control Engineering and Information Technology

Nowadays, more and more common the general-purpose computing on graphics processing units (GPGPU). Tools are available, that make implementing compute-intensive simulations easier on the graphics card, in order to speed them up. We can even work with C++-like programming language. The global illumination algorithms, which can produce wonderful photorealistic images, are fit to this highly paralell environment. The base of these algoritms is the Monte Carlo method, which can handle the error of the multi-dimensional integrals of the rendering process. This error causes initial dot-noise in the image. This noise can be reduced by noise-reduction filters. The problem of these filters is that they cannot determine what is the valid detail and what is the unwanted noise. There is a new algorithm, called Random Parameter Filtering (RPF), which can separate the important details, by searching statistical dependency between the random parameters of the Monte Carlo method and the scene features. We must also use the power of the graphics processor (GPU) for this algorithm.

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