  Recent visual pose estimation and tracking solutions provide notable results
on popular datasets such as T-LESS and YCB. However, in the real world, we can
find ambiguous objects that do not allow exact classification and detection
from a single view. In this work, we propose a framework that, given a single
view of an object, provides the coordinates of a next viewpoint to discriminate
the object against similar ones, if any, and eliminates ambiguities. We also
describe a complete pipeline from a real object's scans to the viewpoint
selection and classification. We validate our approach with a Franka Emika
Panda robot and common household objects featured with ambiguities. We released
the source code to reproduce our experiments.
