# Efficient View Planning Guided by Previous-Session Reconstruction for Repeated Plant Monitoring

arXiv: https://arxiv.org/abs/2510.07028 · HTML: https://mlsanity.com/paper/2510.07028/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Plant and agricultural 3D reconstruction, one of 15 directions in 3D reconstruction and neural rendering.
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Placed in

- 3D reconstruction and neural rendering / [Plant and agricultural 3D reconstruction](https://mlsanity.com/recon3d/plant-and-agricultural-3d-reconstruction/)

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://mlsanity.com/paper/1806.01411/) (2018)
- [HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds](https://mlsanity.com/paper/1906.05332/) (2019)
- [A Survey of Non-Rigid 3D Registration](https://mlsanity.com/paper/2203.07858/) (2022)
- [Supervised Learning of the Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/1905.05833/) (2019)
- [PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention](https://mlsanity.com/paper/2010.16272/) (2020)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts](https://mlsanity.com/paper/2206.10274/) (2022)
- [How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?](https://mlsanity.com/paper/2310.00684/) (2023)
- [4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture](https://mlsanity.com/paper/1610.02482/) (2016)
- [Graph-based View Motion Planning for Fruit Detection](https://mlsanity.com/paper/2303.03048/) (2023)
- [Integrating One-Shot View Planning with a Single Next-Best View via Long-Tail Multiview Sampling](https://mlsanity.com/paper/2304.00910/) (2023)
- [Active Implicit Reconstruction Using One-Shot View Planning](https://mlsanity.com/paper/2310.00685/) (2023)
- [Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes](https://mlsanity.com/paper/2311.16759/) (2023)
- [Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning](https://mlsanity.com/paper/2403.16803/) (2024)
- [GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping](https://mlsanity.com/paper/2503.03912/) (2025)
- [Statistical shape representations for temporal registration of plant components in 3D](https://mlsanity.com/paper/2209.11526/) (2022)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [Enhancing Agricultural Environment Perception via Active Vision and Zero-Shot Learning](https://mlsanity.com/paper/2409.12602/) (2024)
- [SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot](https://mlsanity.com/paper/2410.14790/) (2024)
- [Transformer-Based Spatio-Temporal Association of Apple Fruitlets](https://mlsanity.com/paper/2503.03200/) (2025)

## Abstract (arXiv)

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.