  This work is concerned with a representation of shapes that disentangles
fine, local and possibly repeating geometry, from global, coarse structures.
Achieving such disentanglement leads to two unrelated advantages: i) a
significant compression in the number of parameters required to represent a
given geometry; ii) the ability to manipulate either global geometry, or local
details, without harming the other. At the core of our approach lies a novel
pipeline and neural architecture, which are optimized to represent one specific
atlas, representing one 3D surface. Our pipeline and architecture are designed
so that disentanglement of global geometry from local details is accomplished
through optimization, in a completely unsupervised manner. We show that this
approach achieves better neural shape compression than the state of the art, as
well as enabling manipulation and transfer of shape details. Project page at
http://geometry.cs.ucl.ac.uk/projects/2022/cnnmaps/ .
