中圖分類號： TP393
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.10.003
引用格式： 高晨. 基于圖卷積網格自編碼器的網格參數化[J].信息技術與網絡安全，2020，39(10)：11-17.

Mesh parameterization based on convolutional mesh autoencoders

Abstract： As a basic tool of digital geometry processing, mesh parameterization has a wide range of applications in game entertainment, engineering design, simulation and other fields. Most of the traditional meshing parameterization methods obtain results by solving linear or nonlinear systems. This article presents a mesh parameterization model based on convolutional mesh autoencoders, the network model is generated by the combination of the encoding part of a convolutional mesh autoencoders and the decoding part, and using a class of human face mesh data set as the network training data. The results show that the algorithm is more than 68 percent faster than the SLIM(Scalable Locally Injective Mappings)algorithm and more than four times faster than the PP(Progressive Parameterizations)algorithm, while the parameters of the mesh parameterization are used to ensure the parameterization effect.

Key words : mesh parameterization；convolutional neural network；autoencoders；convolutional mesh autoencoders