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The ability to generate novel, diverse, and realistic 3D shapes along withassociated part semantics and structure is central to many applicationsrequiring high-quality 3D assets or large volumes of realistic training data. Akey challenge towards this goal is how to accommodate diverse shape variations,including both continuous deformations of parts as well as structural ordiscrete alterations which add to, remove from, or modify the shapeconstituents and compositional structure. Such object structure can typicallybe organized into a hierarchy of constituent object parts and relationships,represented as a hierarchy of n-ary graphs. We introduce StructureNet, ahierarchical graph network which (i) can directly encode shapes represented assuch n-ary graphs; (ii) can be robustly trained on large and complex shapefamilies; and (iii) can be used to generate a great diversity of realisticstructured shape geometries. Technically, we accomplish this by drawinginspiration from recent advances in graph neural networks to propose anorder-invariant encoding of n-ary graphs, considering jointly both partgeometry and inter-part relations during network training. We extensivelyevaluate the quality of the learned latent spaces for various shape familiesand show significant advantages over baseline and competing methods. Thelearned latent spaces enable several structure-aware geometry processingapplications, including shape generation and interpolation, shape editing, orshape structure discovery directly from un-annotated images, point clouds, orpartial scans.

StructureNet: Hierarchical Graph Networks for 3D Shape Generation