基于机载LiDAR点云数据的建筑物三维模型重建方法研究

Research on Building 3D Model Reconstruction Method Based on Airborne LiDAR Point Cloud Data

摘要: 为了提高机载LiDAR点云数据的利用率，本文在已有算法的基础上提出了一种基于机载LiDAR点云数据的建筑物三维模型构建方法。该方法实现建筑物三维模型的步骤为：首先，使用布料模拟滤波算法滤除地面点得到非地面点，将非地面点作为输入数据，利用最大类间方差法实现初始建筑物点云提取；其次，利用Alpha Shape算法提取建筑物边缘点以及利用屋顶分割算法提取建筑物屋顶关键点；最后，基于关键点使用SharpGL工具包实现建筑物三维模型重建。使用实测机载liDAR点云数据对本文提出方法进行检验，结果表明本文方法能够有效构建建筑物三维模型，并且模型精度较高。

Abstract: In order to improve the utilization of airborne LiDAR point cloud data, this paper proposes a build-ing 3D model construction method based on existing algorithms. The steps of implementing a 3D model of a building using this method are as follows: first, use a cloth simulation filtering algorithm to filter out ground points and obtain non ground points; using non ground points as input data, use the maximum inter class variance method to extract the initial building point cloud; secondly, the Alpha Shape algorithm is used to extract building edge points and the roof segmentation algorithm is used to extract building roof key points; finally, based on the key points, use the SharpGL toolkit to reconstruct the 3D model of the building. The method proposed in this paper was validated using measured airborne LiDAR point cloud data, and the results showed that the method can effectively construct a three-dimensional model of buildings with high model accuracy.