从稀疏LiDAR数据中重建居民区建筑物

Residential building reconstruction research based on sparse LiDAR data

针对美国新奥尔良地区稀疏的LiDAR(light detecting and ranging)点云数据,提出一种基于此数据的居民区建筑物重建方法。该方法利用最小包围轮廓来描述居民区建筑物形状,在TIN模型的基础上进行屋顶分割,得到属于每个建筑物的屋顶点;然后,基于三角面片的法向量方向信息对其进行聚类,根据法向量之间的关系进行屋顶类型识别和模型匹配,重建居民区建筑物。实验结果表明,该方法在进行居民区建筑物重建时,能达到95%的重建率,且重建所需时间合理,能够满足虚拟现实系统的需要。

Facing the sparse LiDAR （light detecting and ranging） data of the New Orleans area in America, a new residential building model reconstruction method is proposed. The main contributions of this work are the automatic isolation of roof points and the roof type recognition. Using the minimum bounding contour to describe the outline of residential buildings,we are able to automatically identify individual buildings from clustered residential areas. Then, based on the relationship of normal vectors, building types are recognized; through model matching, the buildings models are reconstructed. Experiments show that our method can successfully reconstruct residential buildings given relatively sparse LiDAR samples in a reasonably short time.