  Objects with symmetries are common in our daily life and in industrial
contexts, but are often ignored in the recent literature on 6D pose estimation
from images. In this paper, we study in an analytical way the link between the
symmetries of a 3D object and its appearance in images. We explain why
symmetrical objects can be a challenge when training machine learning
algorithms that aim at estimating their 6D pose from images. We propose an
efficient and simple solution that relies on the normalization of the pose
rotation. Our approach is general and can be used with any 6D pose estimation
algorithm. Moreover, our method is also beneficial for objects that are 'almost
symmetrical', i.e. objects for which only a detail breaks the symmetry. We
validate our approach within a Faster-RCNN framework on a synthetic dataset
made with objects from the T-Less dataset, which exhibit various types of
symmetries, as well as real sequences from T-Less.
