| Abstract |
In many applications surfaces with a large number
of primitives occur. Geometry compression reduces
storage space and transmission time for such
models. A special case is given by polygonal
isosurfaces generated from gridded volume data.
However, most current state-of-the-art geometry
compression systems do not capitalize on the
structure that is characteristic of such
isosurfaces, namely that the surfaces are defined
by a set of vertices on edges of the grid. In a
previous paper we had proposed a compression
method for isosurfaces that is designed to
exploit this feature. In this paper we use the
same coding approach, however, including context
models for the encoding of the symbol streams. We
report improved compression ratios by about 20%
for complex isosurfaces from a CT scan of a human
head. For this data set our new coder
outperformed state-of-the-art general purpose
geometry compression methods by a factor of 2.6
to 3.4 in terms of compression ratio. We also
report results obtained by two predictive coding
methods based on least squares function fitting
and a surface relaxation algorithm. |