Trees are an important part of the cityscape, and 3D models of trees are indispensable for real-time 3D design, construction of virtual geographic environments, and construction of digital twin cities. Current 3D models of trees are reconstructed based on images or model libraries. The former show cluttered triangular network clusters, and the latter are vastly different from the real situation in terms of geometric expression and realism, which makes directly using the reconstructed tree models in the practical applications of smart cities difficult. Therefore, in this paper, a bionic reconstruction method for 3D tree models is proposed based on high-precision laser scanning point cloud data for building realistic scenes in virtual geographic environments, which enables the automated reconstruction of 3D tree models at multiple levels of detail while preserving morphological features.First, a skeleton-based parametric tree model reconstruction method that extracts branch geometry by generalized cylinder fitting and extracts the trunk, main branches, models of fine branches, and crown elements in a hierarchical manner according to the growth parameters of the tree is proposed. Second, the refinement requirements of modeling distinct parts of trees are considered, and a refined tree geometry reconstruction method by integrating the conformal Poisson network and parametric fitting is presented. Finally, the texture mapping method is applied to map the texture of multilevel tree branches automatically to achieve a detailed 3D reconstruction of tree models by considering the texture extension of the tree structure. Based on the laser point cloud acquired with a backpack or station, this method can produce a refined 3D tree model with high accuracy of morphological features.The overall geometric error of the model is better than 10 cm, and the geometric error of the trunk model is better than 3 cm. Under the same data conditions, the method has the highest degree of reproduction of 3D tree morphology and real texture compared with various mainstream tree modeling methods. Based on the results of this paper, the method can further advance the extraction of tree structure information and the calculation of 3D green volume for the realistic 3D China and national strategies such as green low-carbon development, which have great practical value.This paper proposes a 3D bionic reconstruction method for constructing high-fidelity scenes in virtual geographic environments to achieve highly accurate geometric reconstruction and texture mapping of individual tree roots, trunks, branches, and leaves. The core of the method is to consider the requirements of distinct parts of the tree reconstruction at multiple levels of detail and integrate Poisson mesh and parameter fitting to complete the 3D reconstruction of the tree with high accuracy. The experimental results show the proposed tree 3D reconstruction method provides a highly accurate reconstruction of the tree geometry and texture. The research results are used for the accurate extraction of tree parameters, which can provide an important basis for tree structure information extraction, 3D green volume calculation, and realistic modeling and simulation of virtual geographic environments.

remote sensing;3D modeling;tree reconstruction;virtual geographic environments;parametric modeling;laser point cloud