Direct and accurate feature extraction from 3D point clouds of plants using RANSAC
Direct and accurate feature extraction from 3D point clouds of plants using RANSAC
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DOI：
10.1016/j.compag.2021.106240
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发表时间：
2021-06-15
影响因子：
8.3
通讯作者：
Doonan, John H.
中科院分区：
文献类型：
--
作者：
Ghahremani, Morteza;Williams, Kevin;Doonan, John H.
虽然点云有望测量 3D 物体的几何特征，但它们在植物中的应用仍然存在问题。植物是一种三维 (3D) 生物体，其形态复杂，个体之间存在差异，并且随时间而变化。随着技术提高准确性并减少计算时间，3D 点云域中属性的客观测量越来越有吸引力。然而，由于点云数据的离散性、成像噪声和杂乱的背景，点云数据的分析并不简单。在本文中，我们介绍了一种直接分析点云数据植物的稳健方法。为此，我们推广了随机样本一致性（RANSAC）算法来分析3D点云数据，然后用它来模拟不同的植物器官。由于 3D 点云是从多视图立体图像中获得的，因此它们通常受到相当大程度的噪声、失真和分布外点的污染。我们方法的关键是在 3D 点云上使用 RANSAC 算法，使我们的技术对不良异常值更加稳健。我们通过将从模型中提取的估计测量值与从实际植物中获取的手动测量值进行比较，在芸苔属和葡萄树上测试了我们提出的方法。我们提出的方法对于测量的芸苔属分支和茎的直径实现了 R-2 > 0.90，同时对于测量的葡萄藤叶角和芸苔属的分支角度产生了 R-2 > 0.91。在所有情况下，该方法在成像噪声和杂乱背景下都能产生稳定的性能，而传统方法常常无法工作。
While point clouds hold promise for measuring the geometrical features of 3D objects, their application to plants remains problematic. Plants are three dimensional (3D) organisms whose morphology is complex, varies from one individual to another and changes over time. Objective measurement of attributes in 3D point cloud domain is increasingly attractive as techniques improve the accuracy and reduce computational time. Analysis of point cloud data, however, is not straightforward, due to its discrete nature, imaging noise and cluttered background. In this paper, we introduce a robust method for the direct analysis of plants of point cloud data. To this end, we generalise the random sample consensus (RANSAC) algorithm for the analysis of 3D point cloud data and then use it to model different plant organs. Since 3D point clouds are obtained from multi-view stereo images, they are often contaminated with a considerable level of noise, distortions and out-of-distribution points. Key to our approach is the use of the RANSAC algorithm on 3D point cloud, making our technique more robust to undesirable outliers. We tested our proposed method on Brassica and grapevine by comparing the estimated measurements extracted from the models with manual ones taken from the actual plants. Our proposed method achieved R-2 > 0.90 for measured diameters of branches and stems in Brassica while it yielded R-2 > 0.91 for the measured leaf angles of grapevine and branch angles of Brassica. In all cases, the approach produced stable performance under imaging noise and cluttered background while the conventional methods often failed to work.