Abstract
We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and
geometrically interpretable explanations of 3D objects, our
framework also allows us to automatically discover and exploit consistent structure in the data. We demonstrate that
using our method allows predicting shape representations
which can be leveraged for obtaining a consistent parsing
across the instances of a shape collection and constructing
an interpretable shape similarity measure. We also examine
applications for image-based prediction as well as shape
manipulation