Spatially localized deformation components are very useful for shape analysis
and synthesis in 3D geometry processing. Several methods have recently been
developed, with an aim to extract intuitive and interpretable deformation
components. However, these techniques suffer from fundamental limitations
especially for meshes with noise or large-scale deformations, and may not
always be able to identify important deformation components. In this paper we
propose a novel mesh-based autoencoder architecture that is able to cope with
meshes with irregular topology. We introduce sparse regularization in this
framework, which along with convolutional operations, helps localize
deformations. Our framework is capable of extracting localized deformation
components from mesh data sets with large-scale deformations and is robust to
noise. It also provides a nonlinear approach to reconstruction of meshes using
the extracted basis, which is more effective than the current linear
combination approach. Extensive experiments show that our method outperforms
state-of-the-art methods in both qualitative and quantitative evaluations.

本文提出了一种基于网格自编码器的架构，利用稀疏正则化与卷积运算在处理嘈杂数据和大规模形变的网格时提取局部形变成分，此框架提供了一种以非线性的方式进行基础网格重建的方法，实验表明该方法优于目前的线性组合方法