Learning the Next Best View for 3D Point Clouds via Topological Features
This work addresses the challenge of efficiently capturing detailed 3D data for robotics applications, but it appears incremental as it builds on existing reinforcement learning and topological methods.
The paper tackles the problem of optimizing sensor placement for 3D point cloud acquisition by introducing a reinforcement learning approach with a topology-based information gain metric, resulting in improved focus on high-detail features like holes and concave sections, though no concrete numbers are provided.
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.