  The ability to generate novel, diverse, and realistic 3D shapes along with
associated part semantics and structure is central to many applications
requiring high-quality 3D assets or large volumes of realistic training data. A
key challenge towards this goal is how to accommodate diverse shape variations,
including both continuous deformations of parts as well as structural or
discrete alterations which add to, remove from, or modify the shape
constituents and compositional structure. Such object structure can typically
be organized into a hierarchy of constituent object parts and relationships,
represented as a hierarchy of n-ary graphs. We introduce StructureNet, a
hierarchical graph network which (i) can directly encode shapes represented as
such n-ary graphs; (ii) can be robustly trained on large and complex shape
families; and (iii) can be used to generate a great diversity of realistic
structured shape geometries. Technically, we accomplish this by drawing
inspiration from recent advances in graph neural networks to propose an
order-invariant encoding of n-ary graphs, considering jointly both part
geometry and inter-part relations during network training. We extensively
evaluate the quality of the learned latent spaces for various shape families
and show significant advantages over baseline and competing methods. The
learned latent spaces enable several structure-aware geometry processing
applications, including shape generation and interpolation, shape editing, or
shape structure discovery directly from un-annotated images, point clouds, or
partial scans.

Graphics;Computational Geometry;Computer Vision and Pattern Recognition