以车载激光扫描点云数据为研究对象,提出一种由粗到细且快速获取点云中建筑物3维位置边界的方法。首先,通过分析格网内部点云的空间分布特征(平面距离、高程差异和点密集程度等)确定激光扫描点的权值,采用距离加权倒数IDW(Inverse Distance Weighted)内插方法生成车载激光扫描点云的特征图像。然后,采用阈值分割、轮廓提取与跟踪等手段提取特征图像中的建筑物目标的粗糙边界。最后,对粗糙边界内部的建筑物目标点云进行平面分割,提取建筑物的立面特征并构建立面不规则三角网TIN(Triangulated Irregular Network),并在建筑物先验框架知识条件下自动提取建筑物的精确3维位置边界。

This paper presents a novel method for automated extraction of building footprints from mobile LiDAR point clouds.We first generate the georeferenced feature image of mobile LiDAR point clouds using an interpolation method, and adopt imagesegmentation and contour extraction and tracing to extract building boundaries in the geo-referenced feature image as the coarselevel of building footprints in Two-dimensional imagery space. Then, the coarse level of building footprints is further refined byapplying planar segmentation on the extracted point clouds in the building boundaries. Finally, the triangulated irregular network (TIN)is used to achieve the fine level of building footprints. Dataset of residential areas captured by Optech's LYNX mobile mappingsystem was tested to check the validities of the proposed method. Experimental results show that the proposed method provides apromising and valid solution for automatically extracting building footprints from mobile LIDAR point clouds.