  This paper presents a new system to obtain dense object reconstructions along
with 6-DoF poses from a single image. Geared towards high fidelity
reconstruction, several recent approaches leverage implicit surface
representations and deep neural networks to estimate a 3D mesh of an object,
given a single image. However, all such approaches recover only the shape of an
object; the reconstruction is often in a canonical frame, unsuitable for
downstream robotics tasks. To this end, we leverage recent advances in
differentiable rendering (in particular, rasterization) to close the loop with
3D reconstruction in camera frame. We demonstrate that our approach---dubbed
reconstruct, rasterize and backprop (RRB) achieves significantly lower pose
estimation errors compared to prior art, and is able to recover dense object
shapes and poses from imagery. We further extend our results to an (offline)
setup, where we demonstrate a dense monocular object-centric egomotion
estimation system.
