Force field driven skeleton extraction method for point cloud trees

A key step in processing natural trees from point cloud data is to reconstruct the trees’ skeleton, which plays an important role in forest investigation and monitoring. Although the techniques for general objects skeletonizing based on point clouds have made a large stride, there are few efficient and simple studies on the natural trees, which have complex topologies. In this paper, we propose a novel method to reconstruct tree skeletons based on the point cloud data, named as the force field driven tree skeleton extracting method, which consists of the following steps. Specifically, to make the point cloud tree a little bit cleaner, the hierarchical subdivision to the original point cloud space is firstly proposed. Then the split-level of the trees’ applied space is considered in each layers, and a simplified representation of the feature points for the tree model is then established under the neighbor relationships. After that, the feature points in the peripheral are connected by the geodesic distance. To get the initial skeleton, the surface geodesic lines are compressed into the tree model by applying a visible repulsive force field. Finally, the final skeleton is acquired by polishing the initial skeleton according to a threshold setting. The experimental results on two kinds of representative naturally growing trees, which are the Cherry and Michelia, indicate that this method can provide a satisfactory performance.