  On robotics computer vision tasks, generating and annotating large amounts of
data from real-world for the use of deep learning-based approaches is often
difficult or even impossible. A common strategy for solving this problem is to
apply simulation-to-reality (sim2real) approaches with the help of simulated
scenes. While the majority of current robotics vision sim2real work focuses on
image data, we present an industrial application case that uses sim2real
transfer learning for point cloud data. We provide insights on how to generate
and process synthetic point cloud data in order to achieve better performance
when the learned model is transferred to real-world data. The issue of
imbalanced learning is investigated using multiple strategies. A novel
patch-based attention network is proposed additionally to tackle this problem.
