  Pose estimation is a widely explored problem, enabling many robotic tasks
such as grasping and manipulation. In this paper, we tackle the problem of pose
estimation for objects that exhibit rotational symmetry, which are common in
man-made and industrial environments. In particular, our aim is to infer poses
for objects not seen at training time, but for which their 3D CAD models are
available at test time. Previous work has tackled this problem by learning to
compare captured views of real objects with the rendered views of their 3D CAD
models, by embedding them in a joint latent space using neural networks. We
show that sidestepping the issue of symmetry in this scenario during training
leads to poor performance at test time. We propose a model that reasons about
rotational symmetry during training by having access to only a small set of
symmetry-labeled objects, whereby exploiting a large collection of unlabeled
CAD models. We demonstrate that our approach significantly outperforms a
naively trained neural network on a new pose dataset containing images of tools
and hardware.
