  In many automation tasks involving manipulation of rigid objects, the poses
of the objects must be acquired. Vision-based pose estimation using a single
RGB or RGB-D sensor is especially popular due to its broad applicability.
However, single-view pose estimation is inherently limited by depth ambiguity
and ambiguities imposed by various phenomena like occlusion, self-occlusion,
reflections, etc. Aggregation of information from multiple views can
potentially resolve these ambiguities, but the current state-of-the-art
multi-view pose estimation method only uses multiple views to aggregate
single-view pose estimates, and thus rely on obtaining good single-view
estimates. We present a multi-view pose estimation method which aggregates
learned 2D-3D distributions from multiple views for both the initial estimate
and optional refinement. Our method performs probabilistic sampling of 3D-3D
correspondences under epipolar constraints using learned 2D-3D correspondence
distributions which are implicitly trained to respect visual ambiguities such
as symmetry. Evaluation on the T-LESS dataset shows that our method reduces
pose estimation errors by 80-91% compared to the best single-view method, and
we present state-of-the-art results on T-LESS with four views, even compared
with methods using five and eight views.
