Multi-layer compression algorithm for 3D deformed mesh based on multi library wavelet neural network architecture

Abstract

We propose in this paper a 3D mesh compression algorithm for 3D deformation objects to facilitate the transmission of deformed object to another. This algorithm allows eliminating an object in the sequence of deformed objects and reducing the information needed to represent the geometry of a mesh sequence. In our approach, we used Multi Library Wavelet Neural Network architecture (MLWNN) to align features of mesh and minimize distortion with fixed features. The introduced method minimizes the sum of the distances between all the corresponding vertices. It computes deformed ROI (Region Of Interest), updates and optimizes it to align the mesh features. First, our compression was performed using spherical geometrical image obtained by our trust region spherical parameterization. Geometrical images also facilitate compression and level-of-detail control. Second, the spherical wavelet transformation was used to decompose the geometrical image into multi-resolution sub-images characterizing the underlying functions in a local fashion in both spatial and frequency domains. Experimental results show that the progressive compression algorithm yields efficient compression capabilities with minimal set of features used to have good deformation scheme.