  Reconstructing the 3D geometry of an object from an image is a major
challenge in computer vision. Recently introduced differentiable renderers can
be leveraged to learn the 3D geometry of objects from 2D images, but those
approaches require additional supervision to enable the renderer to produce an
output that can be compared to the input image. This can be scene information
or constraints such as object silhouettes, uniform backgrounds, material,
texture, and lighting. In this paper, we propose an approach that enables a
differentiable rendering-based learning of 3D objects from images with
backgrounds without the need for silhouette supervision. Instead of trying to
render an image close to the input, we propose an adversarial style-transfer
and domain adaptation pipeline that allows to translate the input image domain
to the rendered image domain. This allows us to directly compare between a
translated image and the differentiable rendering of a 3D object reconstruction
in order to train the 3D object reconstruction network. We show that the
approach learns 3D geometry from images with backgrounds and provides a better
performance than constrained methods for single-view 3D object reconstruction
on this task.
