Abstract
The highest fidelity representations of realistic real-world materials currently used comprise Bidirectional Texture Functions (BTF). The BTF is a six dimensional function depending on view and illumination directions as well as on planar texture coordinates. The huge size of such measurements, typically in the form of thousands of images covering all possible combinations of illumination and viewing angles, has prohibited their practical exploitation and obviously some compression and modelling method of these enormous BTF data spaces is inevitable. The proposed approach combines BTF spatial clustering with cluster index modelling by means of an efficient Markov random field model. This method allows to generate seamless cluster index of arbitrary size to cover large virtual 3D objects surfaces. The method represents original BTF data using a set of local spatially dependent Bidirectional Reflectance Distribution Function (BRDF) values which are combined according to synthesised cluster index and illumination / viewing directions. BTF data compression using this method is about 1:100 and their synthesis is very fast.
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Filip, J., Haindl, M. (2006). BTF Modelling Using BRDF Texels. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_50
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DOI: https://doi.org/10.1007/11821045_50
Publisher Name: Springer, Berlin, Heidelberg
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