ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

Dominik Bauer, Timothy Patten, Markus Vincze

Abstract:

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. Code is available at github.com/dornik/reagent.