  In recent years, considerable progress has been made for the task of rigid
object pose estimation from a single RGB-image, but achieving robustness to
partial occlusions remains a challenging problem. Pose refinement via rendering
has shown promise in order to achieve improved results, in particular, when
data is scarce.
  In this paper we focus our attention on pose refinement, and show how to push
the state-of-the-art further in the case of partial occlusions. The proposed
pose refinement method leverages on a simplified learning task, where a CNN is
trained to estimate the reprojection error between an observed and a rendered
image. We experiment by training on purely synthetic data as well as a mixture
of synthetic and real data. Current state-of-the-art results are outperformed
for two out of three metrics on the Occlusion LINEMOD benchmark, while
performing on-par for the final metric.
