为了提高泊松表面重建算法效率并改善重建结果细节表现，采用一种基于混合树的点云搜索方法，平衡了八叉树和二叉树技术关于时间复杂度和空间复杂度的冲突; 并在点云搜索阶段通过引入多个能量项对点云进行密度评估与滤波等，针对点云稀疏部分进行自适应的点云稠密化以保证重建模型的细节与准确度。结果表明，混合树重建算法与泊松表面重建算法及屏蔽泊松算法相比，速度分别平均提升了33%和15%，且能更好地保持重建模型的细节，误差最小。该研究为点云的表面重建提供了参考。

To improve the efficiency and detail performance of the Poisson surface reconstruction algorithm’s reconstruction results, a point cloud search method based on a hybrid tree balances the conflict between time complexity and space complexity of octree and binary tree technology. In the point cloud search stage, the density evaluation and filtering of the point cloud were used by introducing multiple energy terms, and adaptive point cloud up sampling was used for the sparse part of the point cloud to ensure the details and accuracy of the reconstructed model. The results show that the speed of the hybrid tree reconstruction algorithm increased by 33% and 15% on average compared with the Poisson surface reconstruction algorithm and the screened Poisson surface reconstruction algorithm. In addition, the details of the reconstructed model can be better maintained to obtain the minimum error. This study provides a reference for the surface reconstruction of point clouds.