  Observational noise, inaccurate segmentation and ambiguity due to symmetry
and occlusion lead to inaccurate object pose estimates. While depth- and
RGB-based pose refinement approaches increase the accuracy of the resulting
pose estimates, they are susceptible to ambiguity in the observation as they
consider visual alignment. We propose to leverage the fact that we often
observe static, rigid scenes. Thus, the objects therein need to be under
physically plausible poses. We show that considering plausibility reduces
ambiguity and, in consequence, allows poses to be more accurately predicted in
cluttered environments. To this end, we extend a recent RL-based registration
approach towards iterative refinement of object poses. Experiments on the
LINEMOD and YCB-VIDEO datasets demonstrate the state-of-the-art performance of
our depth-based refinement approach.
