树木是城市地物的重要组成部分，树木三维模型是实景三维建设、虚拟地理环境构建，以及数字孪生城市建设不可或缺的内容。 当前树木实景三维模型主要基于影像或者模型库的方式进行重建，前者表现为杂乱的三角网团簇，后者在几何表达和真实感方面与真实情况差距较大，这使得重建后的树木模型难以直接用于智慧城市的实际应用。因此，本文面向虚拟地理环境高逼真场景构建需求，提出一种基于高精度激光扫描点云数据的树木三维模型高保真仿生重建方法，可实现形态特征保持的树木三维模型自动化重建。首先提出基于骨架的树木模型参数化重构方法，通过广义圆柱体拟合实现树枝几何形状的抽取，并根据树木生长参数对树干、主要枝条、细小枝条模型以及树冠等要素进行分级提取；其次考虑树木不同部位精细化建模要求，提出泊松构网与参数拟合融合的树木几何模型精细化重建方法，进而基于边界约束条件实现树干与树枝模型的精准拼接与融合；最后采用顾及树木结构的纹理展开方法，对多层级树木枝干进行纹理自动映射贴图，实现高保真的树木模型三维重建。经实验验证，基于背包式或站点式获取的激光点云，本方法可生成形态特征高保真的精细化三维树木模型，模型整体几何误差优于10cm，树干模型误差优于3cm。且在相同数据条件下，与几种主流树木建模方法对比，本方法对树木三维形态和真实纹理的还原度程度最高。基于该研究成果，可进一步实现树木结构信息提取、三维绿量计算，并服务于实景三维中国、绿色低碳发展等国家战略，具有重要的实用价值。

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.