  The most recent trend in estimating the 6D pose of rigid objects has been to
train deep networks to either directly regress the pose from the image or to
predict the 2D locations of 3D keypoints, from which the pose can be obtained
using a PnP algorithm. In both cases, the object is treated as a global entity,
and a single pose estimate is computed. As a consequence, the resulting
techniques can be vulnerable to large occlusions.
  In this paper, we introduce a segmentation-driven 6D pose estimation
framework where each visible part of the objects contributes a local pose
prediction in the form of 2D keypoint locations. We then use a predicted
measure of confidence to combine these pose candidates into a robust set of
3D-to-2D correspondences, from which a reliable pose estimate can be obtained.
We outperform the state-of-the-art on the challenging Occluded-LINEMOD and
YCB-Video datasets, which is evidence that our approach deals well with
multiple poorly-textured objects occluding each other. Furthermore, it relies
on a simple enough architecture to achieve real-time performance.
