# NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction

arXiv: https://arxiv.org/abs/2209.15376 · HTML: https://mlsanity.com/paper/2209.15376/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention](https://mlsanity.com/paper/2010.16272/) (2020)
- [Incremental Object Database: Building 3D Models from Multiple Partial Observations](https://mlsanity.com/paper/1808.00760/) (2018)
- [Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation](https://mlsanity.com/paper/2012.03023/) (2020)
- [Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts](https://mlsanity.com/paper/2206.10274/) (2022)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)
- [Fruit Mapping with Shape Completion for Autonomous Crop Monitoring](https://mlsanity.com/paper/2203.15489/) (2022)
- [Predicting Unobserved Space For Planning via Depth Map Augmentation](https://mlsanity.com/paper/1911.05761/) (2019)

## Built on by (most cited first, 9 of 9)

- [Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts](https://mlsanity.com/paper/2306.09801/) (2023)
- [Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning](https://mlsanity.com/paper/2403.16803/) (2024)
- [EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images](https://mlsanity.com/paper/2503.04441/) (2025)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [Semantic-aware Next-Best-View for Multi-DoFs Mobile System in Search-and-Acquisition based Visual Perception](https://mlsanity.com/paper/2404.16507/) (2024)
- [Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse](https://mlsanity.com/paper/2505.00995/) (2025)
- [HQC-NBV: A Hybrid Quantum-Classical View Planning Approach](https://mlsanity.com/paper/2505.05212/) (2025)
- [SG-DOR: Learning Scene Graphs with Direction-Conditioned Occlusion Reasoning for Pepper Plants](https://mlsanity.com/paper/2603.06512/) (2026)
- [Review and Evaluation of Point-Cloud based Leaf Surface Reconstruction Methods for Agricultural Applications](https://mlsanity.com/paper/2604.03328/) (2026)

## Abstract (arXiv)

Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming at maximizing information gain and covering the fruits in the scene. In this paper, we present a novel viewpoint planning approach that explicitly uses information about the predicted fruit shapes to compute targeted viewpoints that observe as yet unobserved parts of the fruits. Furthermore, we formulate the concept of viewpoint dissimilarity to reduce the sampling space for more efficient selection of useful, dissimilar viewpoints. Our simulation experiments with a UR5e arm equipped with an RGB-D sensor provide a quantitative demonstration of the efficacy of our iterative next best view planning method based on shape completion. In comparative experiments with a state-of-the-art viewpoint planner, we demonstrate improvement not only in the estimation of the fruit sizes, but also in their reconstruction, while significantly reducing the planning time. Finally, we show the viability of our approach for mapping sweet peppers plants with a real robotic system in a commercial glasshouse.