No abstract

Abstract:Morphological antialiasing is a post-processing approach which does note require additional samples computation. This algorithm acts as a non-linear filter, ill-suited to massively parallel hardware architectures. We redesigned the initial method using multiple passes with, in particular, a new approach to line length computation. We also introduce in the method the notion of topological reconstruction to correct the weaknesses of postprocessing antialiasing techniques. Our method runs as a pure post-process filter providing full-image antialiasing at high framerates, competing with traditional MSAA.

Abstract. In computer graphics, global illumination algorithms such as photon mapping require to gather large volumes of data which can be heavily redundant. We propose both a new characterization of useful data and a new optimization method for the photon mapping algorithm using structures borrowed from Artificial Intelligence such as autonomous agents. Our autonomous lighting agents efficiently gather large amounts of useful data and are used to make decisions during rendering. It induces less photons being cast and shorter rendering times in both photon casting and rendering phase of the photon mapping algorithm which leads to an important decrease of memory occupation and slightly shorter rendering times for equal image quality.