FitTree: robust 3d tree mesh reconstruction from point clouds

High-fidelity 3D tree mesh models have broad applications in virtual geographic environments, forest ecology, and digital entertainment. Although laser point clouds provide precise data, existing point cloud-based methods struggle with accuracy and completeness due to tree structural complexity, leaf occlusion, and data gaps. To address these challenges, we propose a novel framework that integrates data-driven and morphology-knowledge-driven techniques for generating high-fidelity tree mesh models with accurate trunk and overall crown morphology. The proposed framework consists of three main stages. First, in the trunk separation stage, non-scattering feature clustering is employed to extract the main trunk points. Second, in the robust trunk mesh generation and optimization stage, a coarse-to-fine skeleton extraction and refinement method is proposed, followed by weighted Levenberg–Marquardt cylindrical fitting to generate the trunk geometry. Finally, we propose a novel method that integrates improved space colonization with Alpha Shape constraints to overcome challenges of reconstructing fine branches within the crown, thereby generating complete tree mesh models with accurate trunk and crown morphology. The method was evaluated on laser-scanned, 3D Gaussian, and synthetic point clouds, and benchmarked against state-of-the-art techniques. Results demonstrate superior accuracy and robustness across diverse point cloud types, with strong resilience to noise and incomplete data.