  3D object detection and pose estimation from a single image are two
inherently ambiguous problems. Oftentimes, objects appear similar from
different viewpoints due to shape symmetries, occlusion and repetitive
textures. This ambiguity in both detection and pose estimation means that an
object instance can be perfectly described by several different poses and even
classes. In this work we propose to explicitly deal with this uncertainty. For
each object instance we predict multiple pose and class outcomes to estimate
the specific pose distribution generated by symmetries and repetitive textures.
The distribution collapses to a single outcome when the visual appearance
uniquely identifies just one valid pose. We show the benefits of our approach
which provides not only a better explanation for pose ambiguity, but also a
higher accuracy in terms of pose estimation.
