  A key goal of computer vision is to recover the underlying 3D structure from
2D observations of the world. In this paper we learn strong deep generative
models of 3D structures, and recover these structures from 3D and 2D images via
probabilistic inference. We demonstrate high-quality samples and report
log-likelihoods on several datasets, including ShapeNet [2], and establish the
first benchmarks in the literature. We also show how these models and their
inference networks can be trained end-to-end from 2D images. This demonstrates
for the first time the feasibility of learning to infer 3D representations of
the world in a purely unsupervised manner.
