山东理工大学学报（自然科学版）

准确、快速地实现建筑物特征提取对智慧城市建设具有重要意义，而面特征和线特征是建筑物最主要的几何信息，可以准确反映建筑物的结构特征。本文分别采用欧式聚类法、随机抽样一致性算法和区域生长法检测面特征，分析最优检测结果；从边缘检测的角度分别基于法线和近似曲率检测线特征，并对比分析线特征提取的效果。实验结果表明，基于区域生长的面特征检测效果较其他方法在细部检测方面效果更好，基于近似曲率的线特征检测更连续、平滑。建筑物特征的精确高效检测对于后续的建筑物模型重建具有一定应用价值。

Accurate and fast building feature extraction is of great significance for construction of smart cities. Polygonal features and linear features are the most fundamental geometric information, which can accurately reveal building structural characteristics. This study first conducted comparative analysis of surface feature detection using three distinct approaches: Euclidean clustering, random sampling consistency algorithm and regional growth method, followed by quantitative performance comparison. For edge characterization, the linear features were detected by normal vector and approximate curvature estimation, respectively, and the effects of them were compared and analyzed. Experiments show that the regional growth method demonstrated superior precision of surface feature extraction compared to other methods, whereas the approximate curvature estimation enhanced continuity of smoothness of linear characteristics. The methods adopted in this study for accurate and efficient detection of building features has considerable application value for subsequent reconstruction of building models.