  The success of various applications including robotics, digital content
creation, and visualization demand a structured and abstract representation of
the 3D world from limited sensor data. Inspired by the nature of human
perception of 3D shapes as a collection of simple parts, we explore such an
abstract shape representation based on primitives. Given a single depth image
of an object, we present 3D-PRNN, a generative recurrent neural network that
synthesizes multiple plausible shapes composed of a set of primitives. Our
generative model encodes symmetry characteristics of common man-made objects,
preserves long-range structural coherence, and describes objects of varying
complexity with a compact representation. We also propose a method based on
Gaussian Fields to generate a large scale dataset of primitive-based shape
representations to train our network. We evaluate our approach on a wide range
of examples and show that it outperforms nearest-neighbor based shape retrieval
methods and is on-par with voxel-based generative models while using a
significantly reduced parameter space.
