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

TL;DR

This study compares nine point-cloud based leaf surface reconstruction methods across diverse datasets, highlighting their strengths and trade-offs for agricultural robotic applications.

Contribution

It provides a comprehensive evaluation of existing methods, offering practical guidance for selecting suitable techniques based on application needs and resource constraints.

Findings

Different methods excel in various aspects like accuracy and noise robustness.

Trade-offs exist between computational cost and reconstruction quality.

Guidelines are provided for choosing methods based on specific application constraints.

Abstract

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.