# Receding-Horizon Next-Best-View Planner for Autonomous Leaf Surface Reconstruction

arXiv: https://arxiv.org/abs/2607.28995 · HTML: https://mlsanity.com/paper/2607.28995/

## 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 12 of its 33 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## 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, 12 of 12)

- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [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)
- [Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/2304.11465/) (2023)
- [PB-NBV: Efficient Projection-Based Next-Best-View Planning Framework for Reconstruction of Unknown Objects](https://mlsanity.com/paper/2501.10663/) (2025)
- [Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes](https://mlsanity.com/paper/2311.16759/) (2023)
- [MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction](https://mlsanity.com/paper/2307.04004/) (2023)
- [GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping](https://mlsanity.com/paper/2503.03912/) (2025)
- [Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)](https://mlsanity.com/paper/2403.00566/) (2024)
- [Review and Evaluation of Point-Cloud based Leaf Surface Reconstruction Methods for Agricultural Applications](https://mlsanity.com/paper/2604.03328/) (2026)

## Abstract (arXiv)

Accurate plant leaf modeling is fundamental to downstream tasks such as plant growth monitoring, and phenotyping for yield estimation. Autonomous robotic reconstruction for large-scale field deployment must address limitations on robot planning budget and computation resources while optimizing viewpoint utility for leaf surface reconstruction. Existing approaches either focus on rigid objects, point-cloud coverage or plant reconstruction without fully addressing the system limitations or exploiting task-driven point cloud utility. In this work, we study next-best-view (NBV) planning for leaf surface reconstruction under travel constraints. We develop a novel Centroid-based Information Gain (CIG) function that measures the spatial distribution of observed points relative to the centroid of the existing point cloud to compute viewpoint utility. We also develop a receding-horizon variant that reasons over future viewpoints. To benchmark our work, we use the LAST-STRAW [1] public dataset that includes point clouds of strawberry plants over different growth stages and compare our method with attention-driven NBV [2] that uses a visibility-based information gain approach. The proposed receding-horizon approach consistently reduces surface reconstruction error and improves geometric fidelity across multiple growth stages, especially under increased inter-leaf occlusion. Results demonstrate that our approach is able to visit viewpoints that reduce surface reconstruction error and improves reconstruc-tion accuracy as compared to the baseline by upto 10%.