Shape compression using spherical geometry images
MINGLE 2003 Workshop.
In Advances in Multiresolution for Geometric Modelling,
N. Dodgson, M. Floater, M. Sabin (eds.), Springer-Verlag, 27-46.
Concise shape description exploiting 2D image compression techniques.
We recently introduced an algorithm for spherical parametrization and remeshing, which allows resampling of
a genus-zero surface onto a regular 2D grid, a spherical geometry image. These geometry images offer
several advantages for shape compression. First, simple extension rules extend the square image domain to
cover the infinite plane, thereby providing a globally smooth surface parametrization. The 2D grid
structure permits use of ordinary image wavelets, including higher-order wavelets with polynomial
precision. The coarsest wavelets span the entire surface and thus encode the lowest frequencies of the
shape. Finally, the compression and decompression algorithms operate on ordinary 2D arrays, and are thus
ideally suited for hardware acceleration. In this paper, we detail two wavelet-based approaches for shape
compression using spherical geometry images, and provide comparisons with previous compression schemes.
It is interesting that we obtain compression rates that are more aggressive than in traditional image
compression. This reflects the fact that the geometry of individual shapes is rather smooth, and in some
sense contains less information than typical images.