# Review and Evaluation of Point-Cloud based Leaf Surface Reconstruction Methods for Agricultural Applications

arXiv: https://arxiv.org/abs/2604.03328 · HTML: https://mlsanity.com/paper/2604.03328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts](https://mlsanity.com/paper/2206.10274/) (2022)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [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)

## Abstract (arXiv)

Accurate reconstruction of leaf surfaces from 3D point cloud is essential for agricultural applications such as phenotyping. However, real-world plant data (i.e., irregular 3D point cloud) are often complex to reconstruct plant parts accurately. A wide range of surface reconstruction methods has been proposed, including parametric, triangulation-based, implicit, and learning based approaches, yet their relative performance for leaf surface reconstruction remains insufficiently understood. In this work, we present a comparative study of nine representative surface reconstruction methods for leaf surfaces. We evaluate these methods on three publicly available datasets: LAST-STRAW, Pheno4D, and Crops3D - spanning diverse species, sensors, and sensing environments, ranging from clean high-resolution indoor scans to noisy low-resolution field settings. The analysis highlights the trade-offs between surface area estimation accuracy, smoothness, robustness to noise and missing data, and computational cost across different methods. These factors affect the cost and constraints of robotic hardware used in agricultural applications. Our results show that each method exhibits distinct advantages depending on application and resource constraints. The findings provide practical guidance for selecting surface reconstruction techniques for resource constrained robotic platforms.