基于点云数据的植物叶片特征提取及三维重建

Feature extraction and 3D reconstruction of plant leaf based on point cloud data

如何准确而高效地获取、处理和重建物体的三维点云数据是制约三维检测技术发展的关键性问题。该文主要以植物叶片为载体,对三维点云的数据获取、特征提取以及曲面重建技术进行研究。针对三维点云的获取问题,采用面结构光三维扫描仪采集植物叶片表面的三维点云数据,进而开展点云数据的预处理,包括去噪、精简和平滑。采用基于曲率和基于点云距离的特征提取方法分别对叶片叶脉信息进行提取,并对两种方法进行对比;引入基于边界点邻域特征的边界提取算法得到植物叶片的边缘轮廓信息。讨论三维点云曲面重建算法及其原理,分析贪婪投影三角化算法和泊松曲面重建算法的效果。结果表明,该文所提出的特征提取和三维重建方法,在合适的参数设置下能够在两种不同的植物叶片上得到一致的效果。

How to accurately and efficiently acquire,process and reconstruct 3D point cloud data of objects is a key issue that restricts the progress of 3D inspection technology.In this paper,the plant leaf is taken to study the acquisition,feature extraction and surface reconstruction of 3D point cloud.For the acquisition of 3D point cloud,the surface structured light 3D scanner is used to collect 3D point cloud data on the surface of plant leaves,and then the point cloud data is preprocessed,including denoising,reducing and smoothing.The feature extraction method based on curvature and point cloud distance is used to extract the blade vein information,and the two methods are compared.The edge contour information of plant leaves is extracted by the boundary extraction algorithm based on the features of the boundary point.The 3D point cloud surface reconstruction algorithm and its principle are studied.The greedy projection triangulation algorithm and the Poisson surface reconstruction algorithm are used to analyze the reconstruction effect.The results show that the proposed feature extraction and surface reconstruction methods could obtain consistent effects on the two different plant leaves if they have proper parameter settings.