  This paper tackles the problem of parts-aware point cloud generation. Unlike
existing works which require the point cloud to be segmented into parts a
priori, our parts-aware editing and generation are performed in an unsupervised
manner. We achieve this with a simple modification of the Variational
Auto-Encoder which yields a joint model of the point cloud itself along with a
schematic representation of it as a combination of shape primitives. In
particular, we introduce a latent representation of the point cloud which can
be decomposed into a disentangled representation for each part of the shape.
These parts are in turn disentangled into both a shape primitive and a point
cloud representation, along with a standardising transformation to a canonical
coordinate system. The dependencies between our standardising transformations
preserve the spatial dependencies between the parts in a manner that allows
meaningful parts-aware point cloud generation and shape editing. In addition to
the flexibility afforded by our disentangled representation, the inductive bias
introduced by our joint modeling approach yields state-of-the-art experimental
results on the ShapeNet dataset.
