GALERNE, B.; LECLAIRE, Arthur; MOISAN, L.

doi:10.1111/cgf.13073

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GALERNE, B.
Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145]

LECLAIRE, Arthur
Centre de Mathématiques et de Leurs Applications [CMLA]

MOISAN, L.
Mathématiques Appliquées Paris 5 [MAP5 - UMR 8145]
Institut de Mathématiques de Bordeaux [IMB]

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Computer Graphics Forum. 2017-01-19

Wiley

Résumé en anglais

Designing realistic noise patterns from scratch is hard. To solve this problem, recent contributions have proposed involved spectral analysis algorithms that enable procedural noise models to faithfully reproduce some class of textures. The aim of this paper is to propose the simplest and most efficient noise model that allows for the reproduction of any Gaussian texture. Texton noise is a simple sparse convolution noise that sums randomly scattered copies of a small bilinear texture called texton. We introduce an automatic algorithm to compute the texton associated with an input texture image that concentrates the input frequency content into the desired texton support. One of the main features of texton noise is that its evaluation only consists to sum thirty texture fetches on average. Consequently texton noise generates Gaussian textures with an unprecedented evaluation speed for noise by example. A second main feature of texton noise is that it allows for high quality on-the-fly anisotropic filtering by simply invoking existing GPU hardware solutions for texture fetches. In addition, we demonstrate that texton noise can be applied on any surface using parameterization-free surface noise and that it allows for noise mixing.< Réduire

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procedural noise

noise by example

texton

Gaussian texture

antialiasing

noise mixing

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Texton Noise

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