Item

Abstract

All global illumination algorithms are based on rendering equation. The rendering equation is solved in different ways in every algorithm. Most of algorithms solve the equation by using Monte Carlo method. In this process many samples are produced. These samples have different contribution to generated image. If one hopes to get acceptable result with fewer samples, important samples, which have more contribution for the nal image, must be considered in the rst place. For example, in ordinary Light Tracing, millions of photons have to be traced in order to obtain the distribution of illumination in the whole scene. Actually only a part of scene can be observed most of the time, and just photons hitting visible surfaces will contribute to the generated image. If only a small part of entire scene is visible, we will spend most of the time tracing and storing unimportant photons that have no any contribution to the nal image. Even considering only visible photons, one can see that their contribution to image is very different. Surfaces that are located closer to viewpoint have larger image plane projected area and thus require more photons to achieve the same noise level as surfaces located further away. Orientation of surface in respect to view direction also affects viewdependent photons importance. Depending on the application and used Monte Carlo algorithm one can come up with many other different criteria to compute this importance, which may dramatically affect the quality of produced images and computation speed. Algorithm presented in the thesis takes only useful (visible) photons into account, concentrating computation only on the surfaces visible by currently active camera, balancing the distribution of photons on the image plane, greatly improving the image quality. Using this concept, we can get better result with fewer photons. In this way it is possible to save not only rendering time, but also storage space because less photons need to be stored. This idea also can be applied in other algorithms where millions of samples have to be generated. Once the difference among these samples is found out, we can pay more attention to the important samples that have more contribution to the result image, while ignoring less important ones, thus using fewer samples to get better result.