Color?mapped noise vector fields for generating procedural micro?patterns

Stochastic micro-patterns successfully enhance the realism of virtual scenes. Procedural models using noise combined with transfer functions are extremely efficient. However, most patterns produced today employ 1D functions, which assign color, transparency, or other material attributes, based solely on single scalar quantity noise. Multi-dimensional have received widespread attention in fields, such as scientific volume rendering. But their potential has not yet been well explored for modeling field procedural texturing. We propose a new model stochastic patterns, defined composition bi-dimensional function (a.k.a. color-map) vector field. Our is versatile, it encompasses several existing noises, including Gaussian and phasor It also generates much larger gamut locally structured notoriously difficult to reproduce. leverage assumption tiling blending algorithm provide real-time generation filtering. A key contribution approximation second order statistics over an arbitrary pixel footprint, enables, addition, filtering normal maps. exhibit wide variety results, profiled waves, concentric non-concentric patterns.