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作 者:张新颖 印志本 张云 雷泽宇 司春景[1] ZHANG Xinying;YIN Zhiben;ZHANG Yun;LEI Zeyu;SI Chunjing(College of Information Engineering,Tarim University,Alar,Xinjiang 843300)
机构地区:[1]塔里木大学信息工程学院,新疆阿拉尔843300
出 处:《塔里木大学学报》2024年第2期77-83,共7页Journal of Tarim University
摘 要:棉花是新疆主要的经济作物之一,采用三维重建法自动获取棉花植株的表型数据,对实时监测棉花生长状态具有重要意义。由于遮挡、光线、分辨率等限制,获取的棉花植株点云不完整,因此本研究使用基于深度学习的点云补全PF-Net模型,对不完整的棉花叶片点云进行几何形态补全研究。首先采集多视角的棉花图像重建三维点云,然后利用聚类、最远点采样、归一化和数据增强等方法构建棉花叶片点云数据集,将数据带入PF-Net模型完成棉花叶片点云几何形态补全。结果表明,3种不同程度的缺失数据(25%,50%和75%)补全后的点云模型倒角距离值分别为11.8×10^(-3),9.9×10^(-3)和10.8×10^(-3),且补全后的棉花叶片点云可以保持原有的几何形态。因此,通过点云补全算法提供棉花植株叶片的完整三维模型,对棉花植株表型自动测量提供完整几何信息具有重要意义。Cotton is one of the main economic pillars in Xinjiang.It is of great significance to obtain phenotypic data of cotton plants automatically by using 3D reconstruction method for real-time monitoring of cotton growth.Due to the limitation of occlusion,light and resolution,the obtained point clouds of cotton plants are incomplete.Therefore,PF-Net model based on deep learning is used to complete the geometry of incomplete cotton leaf point cloud.Firstly,multi-view cotton images were collected to reconstruct the 3D point cloud,and then the cotton leaf point cloud dataset was constructed by clustering,farthest point sampling,normalization and data enhancement,and the data was put into the PF-Net model to complete the geometric form completion of the cotton leaf point cloud.The experimental results show that the chamfering distance values of the point cloud model after completion of three different degrees of missing data(25%,50%and 75%)are 11.8×10^(-3),9.9×10^(-3) and 10.8×10^(-3),respectively,and the cotton leaf point cloud can keep the original geometry after completion.Therefore,the provision of a comprehensive 3D model of cotton plant leaves through the utilization of point cloud completion algorithms holds immense significance in furnishing complete geometric information for the automated measurement of cotton plant phenotypes.
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