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Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

机译：基于非均匀理性基础曲线技术的茶叶点云参数化曲面模型

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摘要

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.
机译：植物叶3D架构在成长期间改变并且显示对环境压力的敏感反应。近年来，叶点云的收购和分割方法迅速发展，但3D建模叶点云层并没有效益大。在该研究中，提出了一种准确拟合茶叶点云的参数化表面建模方法。首先，利用主成分分析来调整点云的姿势和位置。然后，将点云切成多个部分，选择一些部分以产生要装配的点（PSF）。最后，PSF配合到非均匀的Rational B样条（NURBS）表面。开发了两种方法以分别生成有序的PSF和无序PSF。首先将PSF作为B样条表面装配，然后通过最小化拟合误差通过粒子群优化（PSO）解决的拟合误差转化为NURBS形式。拟合误差被指定为拟合表面与点云之间的欧几里德距离和点云子集之间的欧几里德距离的最大值（MV）的加权和。结果表明，即使点云大幅简化（RMSE <1mm，MV <2mm，不执行PSO），也可以使用所提出的建模方法。未来的研究将模拟更广泛的叶子以及不完整的点云。

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