Pedro V. Sander, Steven J. Gortler, John Snyder, and Hugues Hoppe
We create a surface parametrization to store a given surface signal into a texture image. Our goal is to minimize signal approximation error — the difference between the original surface signal and its reconstruction from the sampled texture. We derive a signal-stretch parametrization metric based on 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-to-fine hierarchical solver, further accelerated with a fine-to-coarse propagation of the integrated metric tensor. The resulting parametrizations have increased texture resolution in surface regions with greater signal detail. Compared to traditional geometric parametrizations, the number of texture samples can often be reduced by a factor of 4 for a desired signal accuracy.
|Published in||Eurographics Symposium on Rendering|
Copyright © 2002 by The European Association for Computer Graphics.