In this paper we present a method to optimize the computation of the wavelet transform for the 3D seismic data
while reducing the energy of coefficients to the minimum. This allow us to reduce the entropy of the signal and
so increase the compression ratios. The proposed method exploits the geometrical information contained in the
seismic 3D data to optimize the computation of the wavelet transform. Indeed, the classic filtering is replaced by
a filtering following the horizons contained in the 3D seismic images. Applying this approach in two dimensions
permits us to obtain wavelets coefficients with lowest energy. The experiments show that our method permits
to save extra 8% of the size of the object compared to the classic wavelet transform.
The softwares Mesh and Metro are widely used for measuring geometrical differences between two surfaces.
Unfortunately, those two softwares cannot be used to compute the surface-to-surface distance for huge semiregular
meshes because of the memory capacity. Consequently, estimating the quality of remeshing or geometry
compression algorithms cannot be done for such data. To overcome this problem, we propose an original algorithm
for computing the surface-to-surface distance even for huge semi-regular meshes. The method consists
in exploiting the relevant multi-level structure of a semi-regular mesh for loading successively small regions of
it and computing the symmetrical distance between them and the irregular mesh. Experimentally, the results
obtained with the proposed method are similar to the results obtained with the software MESH, while using a
small memory size. This latter can reach only 2% of the size of the semi-regular mesh. Then, we show that our approach allows to compute the surface-to-surface distance for huge semi-regular meshes.