  There have been recent efforts to learn more meaningful representations via
fixed length codewords from mesh data, since a mesh serves as a complete model
of underlying 3D shape compared to a point cloud. However, the mesh
connectivity presents new difficulties when constructing a deep learning
pipeline for meshes. Previous mesh unsupervised learning approaches typically
assume category-specific templates, e.g., human face/body templates. It
restricts the learned latent codes to only be meaningful for objects in a
specific category, so the learned latent spaces are unable to be used across
different types of objects. In this work, we present WrappingNet, the first
mesh autoencoder enabling general mesh unsupervised learning over heterogeneous
objects. It introduces a novel base graph in the bottleneck dedicated to
representing mesh connectivity, which is shown to facilitate learning a shared
latent space representing object shape. The superiority of WrappingNet mesh
learning is further demonstrated via improved reconstruction quality and
competitive classification compared to point cloud learning, as well as latent
interpolation between meshes of different categories.
