植物结构光点云曲面的重建及参数测量

Surface reconstruction and parameter measurement of plants based on structured light point cloud

随着机器视觉等技术的发展,深度相机在三维建模等方面的应用也越来越广泛。从数学角度推导并分析了Kinect传感器的结构光原理。针对植物的表型曲面重建,设计了使用Kinect获取植物表型的点云数据,间隔一定角度采集图像,通过点云坐标的旋转变换拼接成完整点云图。采用直通滤波和统计滤波法完成植物点云图的滤波,利用贪婪投影三角化法对植物点云集进行曲面重建。最后借助MeshLab实现对所得曲面模型的3D重现以及叶片长度等参数的测量。实验结果表明,重建的植物曲面模型的表型参数与实测值无显著差异,测量结果精度高,相对误差保持在5%以内,所述方法能够满足植物表型三维重建的需求。

With the development of technologies such as machine vision, depth cameras are becoming more and more widely used in 3 D modeling. For the phenotypic surface reconstruction of plants, designed a method to obtain the point cloud data of the plant phenotype by Kinect, and collected images at a certain angle interval and stitched into a complete point cloud image by rotating the point cloud coordinates.Then used straight-through filtering and statistical filtering to complete the filtering of the plant point cloud image.Then used the greedy projection triangulation method to reconstruct the surface of the plant point cloud. Finally, the 3 D reproduction of the obtained surface model was implement with the help of MeshLab software as well as the measurement of blade length parameters. Experimental results show that there is no significant difference between the phenotypic parameters of the reconstructed plant surface model and the measured values, and the relative error is kept within 5%, this method can meet the needs of 3 D reconstruction of plant phenotype.