  Single shot approaches have demonstrated tremendous success on various
computer vision tasks. Finding good parameterizations for 6D object pose
estimation remains an open challenge. In this work, we propose different novel
parameterizations for the output of the neural network for single shot 6D
object pose estimation. Our learning-based approach achieves state-of-the-art
performance on two public benchmark datasets. Furthermore, we demonstrate that
the pose estimates can be used for real-world robotic grasping tasks without
additional ICP refinement.
