{"ID":2858782,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07028","arxiv_id":"2510.07028","title":"Efficient View Planning Guided by Previous-Session Reconstruction for Repeated Plant Monitoring","abstract":"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.","short_abstract":"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 guide...","url_abs":"https://arxiv.org/abs/2510.07028","url_pdf":"https://arxiv.org/pdf/2510.07028v2","authors":"[\"Sicong Pan\",\"Luca Lobefaro\",\"Moein Taherkhani\",\"Xuying Huang\",\"Rohit Menon\",\"Cyrill Stachniss\",\"Maren Bennewitz\"]","published":"2025-10-08T13:57:29Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}