A Study on the Influence of Wavelet Number Change in the Wavelet Neural Network Architecture for 3D Mesh Deformation Using Trust Region Spherical Parameterization

The 3D deformation and simulation process frequently include much iteration of geometric design changes. We propose in this paper a study on the influence of wavelet number change in the wavelet neural network architecture for 3D mesh deformation method. Our approach is focused on creating the series of intermediate objects to have the target object, using trust region spherical parameterization algorithm as a common domain of the source and target objects that minimizing angle and area distortions which assurance bijective 3D spherical parameterization, and we used a multi-library wavelet neural network structure (MLWNN) as an approximation tools for feature alignment between the source and the target models to guarantee a successful deformation process. Experimental results show that the spherical parameterization algorithm preserves angle and area distortion, a MLWNN structure relying on various mother wavelets families (MLWNN) to align mesh features and minimize distortion with fixed features, and the increasing of wavelets number makes it possible to facilitate the features alignment which implies the reduction of the error between the objects thus reducing the rate of deformation to have good deformation scheme.