2025/10/08 by Sicong Pan, Lobefaro, Luca, Pan, Sicong +7
Computer Science · Earth and Planetary Sciences · Environmental Science · #3D Surveying and Cultural Heritage #Computational Geometry and Mesh Generation #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2510.07028
openalex publication_date 2025/10/08 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/29
Repeated plant monitoring is essential for tracking crop growth, and 3D reconstruction enables consistent comparison across monitoring sessions. However, rebuilding a 3D model from scratch in every session is costly and overlooks informative geometry already observed previously. We propose efficient view planning guided by a previous-session reconstruction, which reuses a 3D model from the previous session to improve active perception in the current session. Based on this previous-session reconstruction, our method replaces iterative next-best-view planning with one-shot view planning that selects an informative set of views and computes the globally shortest execution path connecting them. Experiments on real multi-session datasets, including public single-plant scans and a newly collected greenhouse crop-row dataset, show that our method achieves comparable or higher surface coverage with fewer executed views and shorter robot paths than iterative and one-shot baselines.