建筑物单体提取是三维重建关键环节。通过传统过绿指数法分类植被点，再提取单体，存在大量建筑物点被错分为植被点问题，难以保证获取的建筑物单体有较好的完整性。针对该问题，提出了一种改进的建筑物单体提取算法。首先结合点云过绿指数、法向量信息分类出植被点；然后考虑被错分的建筑物点和植被点在空间密度分布上的差异性，采用空间密度检测将错分点识别并重新分类为非植被点；最后通过密度聚类将建筑物单体分割，得到较为完整的建筑物单体。用植被分布差异较大的两组数据进行实验，结果表明本文改进的方法能更为完整地提取建筑物单体，有效避免了过绿指数过度错分现象，在正确性波动较小的情况下，完整性和质量精度明显优于过绿指数法。

Extraction of individual buildings is a key step in 3D reconstruction. hrough the traditional excess green index method to classify vegetation points and then extract individual units results in many building points being incorrectly classified as vegetation points, making it difficult to ensure the integrity of the extracted individual buildings. In response to this issue, an improved algorithm for the extraction of individual buildings was proposed. Firstly, the vegetation points were classified by combining the point cloud green index and normal vector information. Then, considering the difference in spatial density distribution between the misdivided building points and the vegetation points, spatial density detection was used to identify and reclassify the misequinox points as non-vegetation points. Finally, density clustering was used to segment individual buildings, resulting in more complete individual building structures. Experiments with two sets of data having significant differences in vegetation distribution show that the improved method proposed in this paper can extract individual buildings more completely, effectively avoiding excessive misclassification by the excess green index method. The method demonstrates significantly better integrity and quality accuracy than the Excess Green Index method, with minimal fluctuations in correctness.