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Objective: 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 mainly reconstructed based on images or model libraries. The former show cluttered triangular network clusters, and the latter are very different from the real situation in terms of geometric expression and realism, which makes it difficult to directly use the reconstructed tree models in the practical applications of smart cities. Therefore, in this paper, we propose a bionic reconstruction method for 3D tree models 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. Method:First, we propose a skeleton-based parametric tree model reconstruction method that realizes the extraction of 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; second, we consider the refinement requirements of modeling different parts of trees and propose a refined tree geometry reconstruction method by integrating the conformal Poisson network and parametric fitting. Finally, the texture mapping method is applied to automatically map the texture of multi-level tree branches to achieve a detailed three-dimensional 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. Result: The overall geometric error of the model is better than 2 cm, and under the same data conditions, this method has the highest reproduction degree of 3D tree morphology and real texture compared with various mainstream tree modeling methods. Based on this research result, the method can further advance the extraction of tree structure information and calculation of 3D green volume, and serve national strategies such as realistic 3D China and green low-carbon development, which is of great practical value. Conclusion: 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 different parts of the tree reconstruction process 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 that the proposed tree 3D reconstruction method provides a highly accurate reconstruction of the tree geometry and texture. The research results are used for 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.