Signal-Specialized Parametrization

dc.contributor.authorSander, Pedro V.en_US
dc.contributor.authorGortler, Steven J.en_US
dc.contributor.authorSnyder, Johnen_US
dc.contributor.authorHoppe, Huguesen_US
dc.contributor.editorP. Debevec and S. Gibsonen_US
dc.date.accessioned2014-01-27T14:06:09Z
dc.date.available2014-01-27T14:06:09Z
dc.date.issued2002en_US
dc.description.abstractTo reduce memory requirements for texture mapping a model, we build a surface parametrization specialized to its signal (such as color or normal). Intuitively, we want to allocate more texture samples in regions with greater signal detail. Our approach is to minimize signal approximation error - the difference between the original surface signal and its reconstruction from the sampled texture. Specifically, our signal-stretch parametrization metric is derived from a Taylor expansion of signal error. For fast evaluation, this metric is pre-integrated over the surface as a metric tensor. We minimize this nonlinear metric using a novel coarse-tofine hierarchical solver, further accelerated with a fine-to-coarse propagation of the integrated metric tensor. Use of metric tensors permits anisotropic squashing of the parametrization along directions of low signal gradient. Texture area can often be reduced by a factor of 4 for a desired signal accuracy compared to nonspecialized parametrizations.en_US
dc.description.seriesinformationEurographics Workshop on Renderingen_US
dc.identifier.isbn1-58113-534-3en_US
dc.identifier.issn1727-3463en_US
dc.identifier.urihttps://doi.org/10.2312/EGWR/EGWR02/087-098en_US
dc.publisherThe Eurographics Associationen_US
dc.titleSignal-Specialized Parametrizationen_US
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