Extreme Compression and Modeling of Bidirectional Texture Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
BTF modelling using BRDF texels
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
BTF modelling using BRDF texels
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Reconstruction of volumetric surface textures for real-time rendering
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Bidirectional texture function simultaneous autoregressive model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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The bidirectional texture function (BTF) describes texture appearance variations due to varying illumination and viewing conditions. This function is acquired by large number of measurements for all possible combinations of illumination and viewing positions hence some compressed representation of these huge BTF texture data spaces is obviously inevitable. In this paper we present a novel efficient probabilistic model-based method for multispectral BTF texture compression which simultaneously allows its efficient modelling. This representation model is capable of seamless BTF space enlargement and direct implementation inside the graphical card processing unit. The analytical step of the algorithm starts with BTF texture surface estimation followed by the spatial factorization of an input multispectral texture image. Single band-limited factors are independently modelled by their dedicated 3D causal autoregressive models (CAR). We estimate an optimal contextual neighbourhood and parameters for each CAR. Finally the synthesized multiresolution multispectral texture pyramid is collapsed into the required size fine resolution synthetic smooth texture. Resulting BTF is combined in a displacement map filter of the rendering hardware using both multispectral and range information, respectively. The presented model offers immense BTF texture compression ratio which cannot be achieved by any other sampling-based BTF texture synthesis method.