2025/05/07 by Giuseppe Fabio Preziosa, Preziosa, Giuseppe Fabio, Andrea Maria Zanchettin +6
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Optimization and Packing Problems #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · doi:10.48550/arxiv.2505.04228
openalex publication_date 2025/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automating the packing of objects with robots is a key challenge in industrial automation, where efficient object perception plays a fundamental role. This paper focuses on scenarios where precise 3D reconstruction is not required, prioritizing cost-effective and scalable solutions. The proposed Low-Resolution Next Best View (LR-NBV) algorithm leverages a utility function that balances pose redundancy and acquisition density, ensuring efficient object reconstruction. Experimental validation demonstrates that LR-NBV consistently outperforms standard NBV approaches, achieving comparable accuracy with significantly fewer poses. This method proves highly suitable for applications requiring efficiency, scalability, and adaptability without relying on high-precision sensing.