提出一种从地面激光点云数据中提取建筑目标并进行分割的新方法，该方法利用半径渐变的主成分分析法确定各点局部几何特征(最佳半径，法向量、维度特征)；根据几何特征将地面点从原始点云中剔除，将非地面点按距离聚类形成点云簇，并对点云簇进行整体特征分析，识别建筑物目标；依据点的局部特征设置区域增长法生长准则对建筑物目标进行平面分割并对分割结果进行优化。实验结果表明，该方法不仅能快速有效提取大场景中的建筑物目标进行分割，并且解决了传统区域增长法不稳定的问题，提高了建筑物点云平面分割的精确性和可靠性。

A new approach for extract and segment building target from terrestrial laser scanner point clouds is presented. The local geometric features (optimal radius, normal, dimensional feature) of each point are calculated by principal component analysis of progressive radius. The points of ground are removed from original point clouds by geometrical characteristic, so that the rest points can be divided into several point-clusters by distance. The statistics features of each point-cluster are calculated to extract the buildings. A growing rule based on the local geometric features is made to segment the buildings into surfaces. The experimental results show that the proposed method has the ability to extract and segment building form wide scene. Besides, the stability and accuracy of building segmenting by the proposed method is higher than that by traditional region growing method.