In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave sections. Experimental results show that our approach can aid in establishing the placement of a robotic sensor to optimize the information provided by its streaming point cloud data. Furthermore, a labeled dataset of 3D objects, a CAD design for a custom robotic manipulator, and software for the transformation, union, and registration of point clouds has been publicly released to the research community.

中文翻译：

---

通过拓扑功能了解3D点云的下一个最佳视图

在本文中，我们介绍了一种强化学习方法，该方法利用一种新颖的基于拓扑的信息增益度量来指导嘈杂的3D传感器的下一个最佳视图。该度量标准将观察到的表面的不连续部分组合在一起，以专注于高细节特征，例如孔和凹入部分。实验结果表明，我们的方法可以帮助建立机器人传感器的位置，以优化其流式点云数据提供的信息。此外，已向研究社区公开发布了带有标签的3D对象数据集，用于定制机器人操纵器的CAD设计以及用于点云的转换，合并和注册的软件。