Learning the Next Best View for 3D Point Clouds via Topological Features

• URL: http://arxiv.org/abs/2103.02789v1
• Date: Thu, 4 Mar 2021 02:19:12 GMT
• Title: Learning the Next Best View for 3D Point Clouds via Topological Features
• Authors: Christopher Collander, William J. Beksi, Manfred Huber
• Abstract summary: We introduce a reinforcement learning approach 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.
• Score: 4.447259318741305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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