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In response to the slow processing speed and low reconstruction accuracy encountered during the 3D reconstruction of trees, a method for 3D reconstruction of single-tree skeletons using laser point cloud data is proposed. Firstly, a combination filtering method is determined based on the point cloud data type to remove outliers and ground points. Secondly, a hybrid registration algorithm based on ISS (Intrinsic Shape Descriptor) and CPD (Coherent Point Drift algorithm), called IS-CPD ( Intrinsic Shape-Coherent Point Drift), is employed to obtain complete point cloud data for individual trees. Finally, a method combining Laplace contraction of point sets and topological refinement is used to obtain the skeleton, and branch models are constructed using cylinders to achieve 3D skeleton reconstruction. Experimental results show that compared to traditional CPD algorithm, the proposed registration scheme improves accuracy and execution speed by 50% and 95. 8% respectively, with a final reconstruction error of no more than 2. 48%. The research demonstrates the effective reconstruction of the 3D skeleton of individual trees, with results close to the original trees, providing a reference for building digital twin environments of forest trees and forestry resource management. [ABSTRACT FROM AUTHOR]