Hestia: Hierarchical Next-Best-View Exploration for Systematic Intelligent Autonomous Data Collection

• URL: http://arxiv.org/abs/2508.01014v1
• Date: Fri, 01 Aug 2025 18:27:23 GMT
• Title: Hestia: Hierarchical Next-Best-View Exploration for Systematic Intelligent Autonomous Data Collection
• Authors: Cheng-You Lu, Zhuoli Zhuang, Nguyen Thanh Trung Le, Da Xiao, Yu-Cheng Chang, Thomas Do, Srinath Sridhar, Chin-teng Lin,
• Abstract summary: This study introduces Hierarchical Next-Best-View Exploration for Systematic Intelligent Autonomous Data Collection (Hestia)<n>Hestia systematically defines the next-best-view task by proposing core components such as dataset choice, observation design, action space, reward calculation, and learning schemes.<n> Experimental results show that Hestia performs robustly across three datasets translated and object settings in the NVIDIA IsaacLab environment.
• Score: 23.427212631082025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in 3D reconstruction and novel view synthesis have enabled efficient, photorealistic rendering, but the data collection process remains largely manual, making it time-consuming and labor-intensive. To address the challenges, this study introduces Hierarchical Next-Best-View Exploration for Systematic Intelligent Autonomous Data Collection (Hestia), which leverages reinforcement learning to learn a generalizable policy for 5-DoF next-best viewpoint prediction. Unlike prior approaches, Hestia systematically defines the next-best-view task by proposing core components such as dataset choice, observation design, action space, reward calculation, and learning schemes, forming a foundation for the planner. Hestia goes beyond prior next-best-view approaches and traditional capture systems through integration and validation in a real-world setup, where a drone serves as a mobile sensor for active scene exploration. Experimental results show that Hestia performs robustly across three datasets and translated object settings in the NVIDIA IsaacLab environment, and proves feasible for real-world deployment.

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