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.

翻译：3D 计算机视图中, 点云登记是一个根本性的问题。 在本文中, 我们将点云登记作为强化学习的规划问题, 它可以通过试验和错误寻求源和目标点云云之间的转换。 通过将点云登记过程建模为Markov 决策过程( MDP), 我们开发了由变换网络和评价网络组成的点云潜在动态模型。 转换网络的目的是在对点云进行僵硬的变换( 行动) 后预测点云的新变化特征, 而评价网络的目的是预测变换源点云和目标点云云的精确度作为奖赏信号。 一旦点云的动态模型经过培训, 我们使用跨点云登记过程的动态模型( CEM) 来通过在点云登记过程中实现最大程度的回报来反复更新规划政策。 因此, 最佳政策, 即源云和目标点云的变换, 可以通过逐步缩小变换的搜索空间( 动作) 获得。 模型Net40 和 7Scen基准数据集的实验结果显示, 我们的方法可以以非监督的方式产生良好的注册表现。