Renderização com amostragem adaptativa no domínio N-dimensional

Abstract

This work proposes improvements in a multidimensional adaptive sampling technique for rendering. Rendering is the process of synthesizing images by algorithms simulating lighting in virtual scenes. The more general techniques of photorealistic rendering — those seeking images that resemble photographs — use integration methods based on Monte Carlo to solve the equation that describes the distribution of light in the scene (rendering equation). Being a probabilistic method which uses randomly generated samples, Monte Carlo produces noise in the final image — result of samples’ variance — and therefore may require a large amount of samples to reduce the noise to acceptable levels. To obtain images of better quality with a lower number of samples, adaptive sampling techniques were proposed, concentrating sampling effort in the most important regions. In this work, we propose the addition of two steps to a multidimensional adaptive sampling technique: substitution of samples and auxiliary integration. These steps aim to give more strength to the technique, enabling their use in a wider variety of situations.