Logs Of 3D Image Reconstruction Based On 3D Laser Scanning Technology

With the development of three-dimensional laser scanning technology,the application of three-dimensional modeling and real-world copying has experienced more and more decades of development.From the beginning of the civil engineering,the application of road,construction to the current restoration of ancient architecture,The realization of visualization of crops and other research in agriculture.Ecological civilization construction has always been the cornerstone of our country's sustainable development.Monitoring the growth of forest resources and realizing statistical management of forest resources has always been a developmental measure for the protection of forest resources in China.The application of three-dimensional laser scanning technology to tree modeling has been forestry.The inevitable trend of development.In order to monitor the growth of forest resources in real time and healthily,this paper takes logs as test samples and scans the logs by using 3D Laser Scanning Technology to get the point cloud data of log,a total of 1016990 data points.Because there is noise and the amount of data is too large,the data need to be denoised and streamlined,and K-means clustering algorithm is used for data segmentation,Self Organization Map(SOM)neural network for data denoising,Chord Height Offset Algorithm for data reduction,564821 data points were obtained,and the three-dimensional model of log was reconstructed based on the processed point cloud data.The experimental results show that the obtained 3D reconstructed image of the log is clear,preserves the complete feature information of the surface,and then evaluates the growth status of the tree.This method can also be applied to remote sensing satellite image processing.At the same time,according to the reconstructed three-dimensional image of the log,it calculates the tree-running factors such as DBH,volume,and surface area,realizes the visualization of the trees,and lays a foundation for the research on the bark texture recognition and the establishment of database for forest data.