ScanBot: Autonomous Reconstruction via Deep Reinforcement Learning

Abstract

Autoscanning of an unknown environment is the key to many AR/VR and robotic applications. However, autonomous reconstruction with both high efficiency and quality remains a challenging problem. In this work, we propose a reconstruction-oriented autoscanning approach, called ScanBot, which utilizes hierarchical deep reinforcement learning techniques for global region-of-interest (ROI) planning to improve the scanning efficiency and local next-best-view (NBV) planning to enhance the reconstruction quality. Given the partially reconstructed scene, the global policy designates an ROI with insufficient exploration or reconstruction. The local policy is then applied to refine the reconstruction quality of objects in this region by planning and scanning a series of NBVs. A novel mixed 2D-3D representation is designed for these policies, where a 2D quality map with tailored quality channels encoding the scanning progress is consumed by the global policy, and a coarse-to-fine 3D volumetric representation that embodies both local environment and object completeness is fed to the local policy. These two policies iterate until the whole scene has been completely explored and scanned. To speed up the learning of complex environmental dynamics and enhance the agent's memory for spatial-temporal inference, we further introduce two novel auxiliary learning tasks to guide the training of our global policy. Thorough evaluations and comparisons are carried out to show the feasibility of our proposed approach and its advantages over previous methods. Code and data are available at https://github.com/HezhiCao/Scanbot.