  Using generative models for Inverse Graphics is an active area of research.
However, most works focus on developing models for supervised and
semi-supervised methods. In this paper, we study the problem of unsupervised
learning of 3D geometry from single images. Our approach is to use a generative
model that produces 2-D images as projections of a latent 3D voxel grid, which
we train either as a variational auto-encoder or using adversarial methods. Our
contributions are as follows: First, we show how to recover 3D shape and pose
from general datasets such as MNIST, and MNIST Fashion in good quality. Second,
we compare the shapes learned using adversarial and variational methods.
Adversarial approach gives denser 3D shapes. Third, we explore the idea of
modelling the pose of an object as uniform distribution to recover 3D shape
from a single image. Our experiment with the CelebA dataset
\cite{liu2015faceattributes} proves that we can recover complete 3D shape from
a single image when the object is symmetric along one, or more axis whilst
results obtained using ModelNet40 \cite{wu20153d} show the potential
side-effects, in which the model learns 3D shapes such that it can render the
same image from any viewpoint. Forth, we present a general end-to-end approach
to learning 3D shapes from single images in a completely unsupervised fashion
by modelling the factors of variation such as azimuth as independent latent
variables. Our method makes no assumptions about the dataset, and can work with
synthetic as well as real images (i.e. unsupervised in true sense). We present
our results, by training the model using the $\mu$-VAE objective
\cite{ucar2019bridging} and a dataset combining all images from MNIST, MNIST
Fashion, CelebA and six categories of ModelNet40. The model is able to learn 3D
shapes and the pose in qood quality and leverages information learned across
all datasets.
