基于激光点云数据的单木骨架三维重构

3D Reconstruction of Single Wood Skeleton Based on Laser Point Cloud Data

针对树木三维重构过程中面临的处理速度慢、重构精度低等问题,提出一种采用激光点云数据的单木骨架三维重构方法。首先,根据点云数据类型确定组合滤波方式,以去除离群点和地面点;其次,采用一种基于内部形态描述子(ISS)和相干点漂移算法(CPD)的混合配准算法(Intrinsic Shape-Coherent Point Drift,IS-CPD),以获取单棵树木的完整点云数据;最后,采用Laplace收缩点集和拓扑细化相结合的方法提取骨架,并通过柱体构建枝干模型,实现骨架三维重构。试验结果表明,相比传统CPD算法,研究设计的配准方案精度和执行速度分别提高50%和95.8%,最终重构误差不超过2.48%。研究结果证明可有效地重构单棵树木的三维骨架,效果接近树木原型,为构建林木数字孪生环境和林业资源管理提供参考。

In response to the slow processing speed and low reconstruction accuracy encountered during the 3D reconstruction of trees,a method for 3D reconstruction of single-tree skeletons using laser point cloud data is proposed.Firstly,a combination filtering method is determined based on the point cloud data type to remove outliers and ground points.Secondly,a hybrid registration algorithm based on ISS(Intrinsic Shape Descriptor) and CPD(Coherent Point Drift algorithm),called IS-CPD(Intrinsic Shape-Coherent Point Drift),is employed to obtain complete point cloud data for individual trees.Finally,a method combining Laplace contraction of point sets and topological refinement is used to obtain the skeleton,and branch models are constructed using cylinders to achieve 3D skeleton reconstruction.Experimental results show that compared to traditional CPD algorithm,the proposed registration scheme improves accuracy and execution speed by 50% and 95.8% respectively,with a final reconstruction error of no more than 2.48%.The research demonstrates the effective reconstruction of the 3D skeleton of individual trees,with results close to the original trees,providing a reference for building digital twin environments of forest trees and forestry resource management.