Mesh-Based Autoencoders for Localized Deformation Component Analysis

Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, Shihong Xia

Abstract:

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.