  In recent years, synthetic data has been widely used in the training of 6D
pose estimation networks, in part because it automatically provides perfect
annotation at low cost. However, there are still non-trivial domain gaps, such
as differences in textures/materials, between synthetic and real data. These
gaps have a measurable impact on performance. To solve this problem, we
introduce a simulation to reality (sim2real) instance-level style transfer for
6D pose estimation network training. Our approach transfers the style of target
objects individually, from synthetic to real, without human intervention. This
improves the quality of synthetic data for training pose estimation networks.
We also propose a complete pipeline from data collection to the training of a
pose estimation network and conduct extensive evaluation on a real-world
robotic platform. Our evaluation shows significant improvement achieved by our
method in both pose estimation performance and the realism of images adapted by
the style transfer.
