  Object pose estimation enables robots to understand and interact with their
environments. Training with synthetic data is necessary in order to adapt to
novel situations. Unfortunately, pose estimation under domain shift, i.e.,
training on synthetic data and testing in the real world, is challenging. Deep
learning-based approaches currently perform best when using encoder-decoder
networks but typically do not generalize to new scenarios with different scene
characteristics. We argue that patch-based approaches, instead of
encoder-decoder networks, are more suited for synthetic-to-real transfer
because local to global object information is better represented. To that end,
we present a novel approach based on a specialized feature pyramid network to
compute multi-scale features for creating pose hypotheses on different feature
map resolutions in parallel. Our single-shot pose estimation approach is
evaluated on multiple standard datasets and outperforms the state of the art by
up to 35%. We also perform grasping experiments in the real world to
demonstrate the advantage of using synthetic data to generalize to novel
environments.
