  Since the introduction of modern deep learning methods for object pose
estimation, test accuracy and efficiency has increased significantly. For
training, however, large amounts of annotated training data are required for
good performance. While the use of synthetic training data prevents the need
for manual annotation, there is currently a large performance gap between
methods trained on real and synthetic data. This paper introduces a new method,
which bridges this gap.
  Most methods trained on synthetic data use 2D images, as domain randomization
in 2D is more developed. To obtain precise poses, many of these methods perform
a final refinement using 3D data. Our method integrates the 3D data into the
network to increase the accuracy of the pose estimation. To allow for domain
randomization in 3D, a sensor-based data augmentation has been developed.
Additionally, we introduce the SparseEdge feature, which uses a wider search
space during point cloud propagation to avoid relying on specific features
without increasing run-time.
  Experiments on three large pose estimation benchmarks show that the presented
method outperforms previous methods trained on synthetic data and achieves
comparable results to existing methods trained on real data.
