Sampling Gabor noise in the spatial domain

Abstract

Gabor noise is a powerful technique for procedural texture generation. Contrary to other types of procedural noise, its sparse convolution aspect makes it easily controllable locally. In this paper, we demonstrate this property by explicitly introducing spatial variations. We do so by linking the sparse convolution process to the parameterization of the underlying surface. Using this approach, it is possible to provide control maps for the parameters in a natural and convenient way. In order to derive intuitive control of the resulting textures, we accomplish a small study of the influence of the parameters of the Gabor kernel with respect to the outcome and we introduce a solution where we bind values such as the frequency or the orientation of the Gabor kernel to a user-provided control map in order to produce novel visual effects.