Compression and Real-Time Rendering of Measured BTFs Using Local PCA
Abstract
The Bidirectional Texture Function (BTF) is a suitable representation for the appearance of highly detailed surface structures under varying illumination and viewing conditions. Real-time rendering of measurements from this six-dimensional function requires approximation strategies, because of the huge size of the dataset.
In this paper we present a framework for BTF-compression and rendering enabling high-quality real-time rendering using much less memory than other comparable data-driven approaches. Our method exploits a BRDF-wise arrangement of the data and employs a flexible generalization of Principal Component Analysis (PCA) named local PCA for the data compression.
Stichwörter: BTF rendering, data compression, material representation, real-time rendering, textures
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Bibtex
@INPROCEEDINGS{mueller-2003-compression, author = {M{\"u}ller, Gero and Meseth, Jan and Klein, Reinhard}, editor = {Ertl, T. and Girod, B. and Greiner, G. and Niemann, H. and Seidel, H.-P. and Steinbach, E. and Westermann, R.}, pages = {271--280}, title = {Compression and Real-Time Rendering of Measured BTFs Using Local PCA}, booktitle = {Vision, Modeling and Visualisation 2003}, year = {2003}, month = nov, publisher = {Akademische Verlagsgesellschaft Aka GmbH, Berlin}, keywords = {BTF rendering, data compression, material representation, real-time rendering, textures}, abstract = {The Bidirectional Texture Function (BTF) is a suitable representation for the appearance of highly detailed surface structures under varying illumination and viewing conditions. Real-time rendering of measurements from this six-dimensional function requires approximation strategies, because of the huge size of the dataset. In this paper we present a framework for BTF-compression and rendering enabling high-quality real-time rendering using much less memory than other comparable data-driven approaches. Our method exploits a BRDF-wise arrangement of the data and employs a flexible generalization of Principal Component Analysis (PCA) named local PCA for the data compression.}, isbn = {3-89838-048-3}, conference = {The 8th International Fall Workshop Vision, Modeling and Visualisation 2003} }