BTF image space utmost compression and modelling method

Abstract

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.