  This paper addresses the task of estimating the 6D pose of a known 3D object
from a single RGB-D image. Most modern approaches solve this task in three
steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii)
Select and refine a pose from the pool. This work focuses on the second step.
While all existing approaches generate the hypotheses pool via local reasoning,
e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is
beneficial at this stage. In particular, we formulate a novel fully-connected
Conditional Random Field (CRF) that outputs a very small number of
pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian,
we give a new and efficient two-step optimization procedure, with some
guarantees for optimality. We utilize our global hypotheses generation
procedure to produce results that exceed state-of-the-art for the challenging
"Occluded Object Dataset".

Computer Vision and Pattern Recognition