In this paper, we propose a new 3D high mesh deformation technique to extract intuitive and interpretable deformation and alignment components. Our framework is based on a fast Beta wavelet transform for a multi-resolution analysis relying on multi-library wavelet neural network architecture. The main drawback of 3D high mesh deformation is the large number of triangles necessary to characterize a smooth surface; the majority of these techniques impose a very high computational cost. Our approach is based on the idea of combining the decomposition technique of multi-resolution analysis by Beta wavelet transform for each level of deformation process and a multi-mother wavelet network structure to construct an effective 3D alignment algorithm. We use, in our experiment, only the approximation coefficients at a chosen decomposition level to reduce the complexity of the mesh and to facilitate the alignment until reaching the target mesh, for the purpose of improving various executions and obtaining an optimal solution while reducing the error between the original and the reconstructed object to create a well-formed object. Then, to enhance the performance of wavelet networks, a novel learning algorithm based on multi-mother wavelet neural network architecture using trust region spherical is employed as an approximation tool for feature alignment between the source and the target models. This network architecture ensures the use of several mother wavelets to solve the problem of high mesh deformation utilizing the best wavelet mother that well models the object. Extensive experimental results demonstrate that the progressive deformation processes aim at avoiding the weaknesses of traditional approaches such as the slowness and the difficulty of finding an exact reconstruction.

在本文中，我们提出了一种新的3D高网格变形技术，以提取直观且可解释的变形和对齐分量。我们的框架基于快速Beta小波变换，用于依赖于多库小波神经网络体系结构的多分辨率分析。3D高网格变形的主要缺点是表征光滑表面所需的大量三角形；这些技术中的大多数强加了很高的计算成本。我们的方法基于将针对每个变形过程的Beta小波变换的多分辨率分析的分解技术与多母小波网络结构相结合以构造有效的3D对齐算法的思想。在实验中，我们使用 为了改善各种执行并获得最佳解决方案，同时减少原始对象与重建对象之间的误差，仅在选定分解级别处的近似系数可以降低网格的复杂度并有助于对齐，直到到达目标网格为止。创建一个格式正确的对象。然后，为了提高小波网络的性能，采用基于多母小波神经网络架构的新的学习算法，该算法使用信任区域球体作为源和目标模型之间特征对准的近似工具。该网络体系结构可确保使用多个母小波来解决问题，并利用能够很好建模对象的最佳小波母来解决高网格变形问题。