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.
In this paper, we propose an efficient geometry compression method
well-adapted to densely sampled semi-regular triangular mesh. Based on
multiresolution analysis performed by wavelet transform, it includes a
low complexity model-based bit allocation across wavelet subbands. The
main contribution of this paper is the resolution of the sub-optimal
bit allocation problem related to biorthogonal wavelet coders for 3D triangular meshes. Indeed, using biorthogonal filters weights the MSE distortion of the reconstructed quantized mesh. These weights can be computed from the wavelet filter bank. This permits to obtain a simple but efficient surface adapted distortion criterion for 3D wavelet coefficient coordinates, and to highly improve the perfomances of MSE-based codecs.