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3D object detection and pose estimation from a single image are twoinherently ambiguous problems. Oftentimes, objects appear similar fromdifferent viewpoints due to shape symmetries, occlusion and repetitivetextures. This ambiguity in both detection and pose estimation means that anobject instance can be perfectly described by several different poses and evenclasses. In this work we propose to explicitly deal with this uncertainty. Foreach object instance we predict multiple pose and class outcomes to estimatethe specific pose distribution generated by symmetries and repetitive textures.The distribution collapses to a single outcome when the visual appearanceuniquely identifies just one valid pose. We show the benefits of our approachwhich provides not only a better explanation for pose ambiguity, but also ahigher accuracy in terms of pose estimation.

Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data