Extracting building roofs from LiDAR data is a key processing step of 3D building model recon-struction. In this paper, we present a progressive method for accurately extracting building roofs with com-plex shapes from airborne LiDAR data. Our method first segments original LiDAR data into a set of rough roofs, with larger area and distinct edges, by the region growing algorithm with a normal threshold and a curvature threshold. Accurate roofs are extracted based on estimating plane equations of the rough roofs us-ing the principal component analysis technique. Our method finally employs the Random Sample Consensus algorithm (RANSAC) to extract smaller roofs from the LiDAR data removed the points belong to the ex-tracted roofs. Experimental results show that the method can robustly extract accurate building roofs in a progressive way from the sampled data by adjusting the thresholds dynamically.%由LiDAR点云数据准确提取建筑物顶面是实现三维建筑模型自动重建的关键步骤.在分析现有顶面提取方法的基础上,提出一种渐进地提取LiDAR点云数据中精细建筑物顶面的方法.先以法向阈值和曲率阈值为约束,借助区域生长算法对原始点云进行初步分割,并得到面积较大、边界特征较明显的初始顶面;再借助主元分析法估算每个初始顶面的平面方程,并以点到平面的距离为约束,利用基于距离的区域生长算法提取其对应的精确顶面;最后通过随机抽样一致性算法(RANSAC)迭代地提取剩余点云中的小顶面.实验表明,通过动态调整阈值和迭代步骤,能够从LiDAR数据中精确地提取出复杂建筑物的顶面.
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