利用前次重建结果，一次规划高效视角，减少重复扫描。
Efficient View Planning Guided by Previous-Session Reconstruction for Repeated Plant Monitoring
- 基于前次3D重建，一次性规划最优观测视角
- 视图减少30%以上，路径缩短40%，覆盖率达95%+
- 适合长期植物监测、农业机器人等场景
重复植物监测对追踪作物生长至关重要，3D重建可实现多时段一致性对比。然而每次会话从头重建3D模型成本高，且忽略此前已观测的有用几何信息。本文提出一种基于前次重建的高效视角规划方法，复用上一周期的3D模型，提升当前周期主动感知效率。基于此重建结果，将迭代式最佳视角规划替换为一次性视角规划，一次性选出高信息量视图，并计算连接它们的全局最短执行路径。在真实多周期数据集上的实验表明，包括公开单株扫描数据和新采集的温室作物行数据集，该方法在视图数量减少30%以上、路径长度缩短40%的前提下，仍能实现与基线相当或更高的表面覆盖率（>95%），显著优于迭代与一次性基线方法。
原文摘要 · Abstract (English)
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.
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