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Bidirectional Texture Function Three Dimensional Pseudo Gaussian Markov Random Field Model

   Michal Havlíček
Abstract: 
The Bidirectional Texture Function (BTF) is the recent most advanced representation of material surface visual properties. BTF specifies the changes of its visual appearance due to varying illumination and viewing angles. Such a function might be represented by thousands of images of given material surface. Original data cannot be used due to its size and some compression is necessary. This paper presents a novel probabilistic model for BTF textures. The method combines synthesized smooth texture and corresponding range map to produce the required BTF texture. Proposed scheme enables very high BTF texture compression ratio and may be used to reconstruct BTF space as well.
Keywords: 
BTF,  texture analysis,  texture synthesis,  data compression,  virtual reality

Results
We have tested BTF 3D PGMRF model on BTF colour textures from the University of Bonn BTF measurements which represents the most accurate ones available to date. Every material in the database is represented by 6561 images, 800 × 800 RGB pixels each, corresponding to 81 × 81 different view and illumination angles respectively. The open source project Blender with plugin for BTF texture support was used to render the results. Very simple scene consisting one source of light one three dimensional object represented by polygons and one camera (its coordinates defines view angles) was rendered several times with varying illumination angles while view angles stayed fixed. Synthetic smooth texture combined with range map in displacement mapping filter of Blender was mapped on the object. Several examples may be reviewed on following figure where visual quality of synthesised BTF may be compared with measured BTF.
The model was also tested on colour textures picked from Amsterdam Library of Textures (ALOT) which consists more coloured, but less dense sampled materials.

Reference: 
Havlíček, M., "Bidirectional Texture Function Three Dimensional Pseudo Gaussian Markov Random Field Model", Doktorandské dny 2012, Praha, České vysoké učení technické v Praze, pp. 53-62, 11/2012.