  We present 3DP3, a framework for inverse graphics that uses inference in a
structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel
models to represent the 3D shape of objects, (ii) hierarchical scene graphs to
decompose scenes into objects and the contacts between them, and (iii) depth
image likelihoods based on real-time graphics. Given an observed RGB-D image,
3DP3's inference algorithm infers the underlying latent 3D scene, including the
object poses and a parsimonious joint parametrization of these poses, using
fast bottom-up pose proposals, novel involutive MCMC updates of the scene graph
structure, and, optionally, neural object detectors and pose estimators. We
show that 3DP3 enables scene understanding that is aware of 3D shape,
occlusion, and contact structure. Our results demonstrate that 3DP3 is more
accurate at 6DoF object pose estimation from real images than deep learning
baselines and shows better generalization to challenging scenes with novel
viewpoints, contact, and partial observability.
