  Point cloud registration is a common step in many 3D computer vision tasks
such as object pose estimation, where a 3D model is aligned to an observation.
Classical registration methods generalize well to novel domains but fail when
given a noisy observation or a bad initialization. Learning-based methods, in
contrast, are more robust but lack in generalization capacity. We propose to
consider iterative point cloud registration as a reinforcement learning task
and, to this end, present a novel registration agent (ReAgent). We employ
imitation learning to initialize its discrete registration policy based on a
steady expert policy. Integration with policy optimization, based on our
proposed alignment reward, further improves the agent's registration
performance. We compare our approach to classical and learning-based
registration methods on both ModelNet40 (synthetic) and ScanObjectNN (real
data) and show that our ReAgent achieves state-of-the-art accuracy. The
lightweight architecture of the agent, moreover, enables reduced inference time
as compared to related approaches. In addition, we apply our method to the
object pose estimation task on real data (LINEMOD), outperforming
state-of-the-art pose refinement approaches.
