  While much progress has been made in 6-DoF object pose estimation from a
single RGB image, the current leading approaches heavily rely on
real-annotation data. As such, they remain sensitive to severe occlusions,
because covering all possible occlusions with annotated data is intractable. In
this paper, we introduce an approach to robustly and accurately estimate the
6-DoF pose in challenging conditions and without using any real pose
annotations. To this end, we leverage the intuition that the poses predicted by
a network from an image and from its counterpart synthetically altered to mimic
occlusion should be consistent, and translate this to a self-supervised loss
function. Our experiments on LINEMOD, Occluded-LINEMOD, YCB and new
Randomization LINEMOD dataset evidence the robustness of our approach. We
achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without
real-pose setting, even outperforming methods that rely on real annotations
during training on Occluded-LINEMOD.
