  Point cloud registration is a fundamental problem in 3D computer vision. In
this paper, we cast point cloud registration into a planning problem in
reinforcement learning, which can seek the transformation between the source
and target point clouds through trial and error. By modeling the point cloud
registration process as a Markov decision process (MDP), we develop a latent
dynamic model of point clouds, consisting of a transformation network and
evaluation network. The transformation network aims to predict the new
transformed feature of the point cloud after performing a rigid transformation
(i.e., action) on it while the evaluation network aims to predict the alignment
precision between the transformed source point cloud and target point cloud as
the reward signal. Once the dynamic model of the point cloud is trained, we
employ the cross-entropy method (CEM) to iteratively update the planning policy
by maximizing the rewards in the point cloud registration process. Thus, the
optimal policy, i.e., the transformation between the source and target point
clouds, can be obtained via gradually narrowing the search space of the
transformation. Experimental results on ModelNet40 and 7Scene benchmark
datasets demonstrate that our method can yield good registration performance in
an unsupervised manner.
