Research on geometric features and point cloud properties for tree skeleton extraction

Abstract

To solve skeleton extraction problems in the tree point cloud model, branch geometric features and local properties of point cloud are utilized to optimize tree skeleton extraction. First of all, according to the attribute information estimation and normal vector adjustment of point cloud neighbor domain, branch segmentation is made by estimated values and geometric features. Skeleton nodes are extracted in the branch subset in segmentations. Then, a graph is constructed based on skeleton node set and tree skeleton is reconstructed in this weighted directed graph. Finally, according to the tree growth characteristics, cubic Hermite curves are utilized to optimize the skeleton curve. This method is applied in the point cloud model of three-kind trees and it is compared with the skeleton extraction method based on voxel switch and point cloud contraction. The experiment results show that this method displays strong anti-interference and high-precision characteristics at branch bifurcation and crossed ending parts of fine tree branches. Thus, features of tree branches can be described more perfectly, obtaining the skeleton curve closer to the main axis.