Roof plane extraction from airborne lidar point clouds
Roof plane extraction from airborne lidar point clouds
复制标题
DOI：
10.1080/01431161.2017.1302112
复制
发表时间：
2017-01-01
影响因子：
3.4
通讯作者：
Zhao, Zongze
中科院分区：
文献类型：
--
作者：
Cao, Rujun;Zhang, Yongjun;Zhao, Zongze
平面面片是多面体建筑模型的重要基元。从机载激光雷达点云数据成功重建建筑物三维模型的关键问题之一是实现屋顶平面点的高质量识别和分割。不幸的是，目前的平面表面的自动提取过程继续遭受的限制，如种子点的选择和缺乏计算效率的敏感性。为了解决这些问题，本文提出了一种新的全自动分割方法，该方法具有以下功能：（1）处理任意形状的屋顶点数据集;（2）在降维的参数空间中鲁棒地选择种子点;（3）在对象空间中进行区域生长时，分割具有相似属性的子数据集中的平面块。通过将累加器阵列映射到一维空间，改进了参数空间中种子点的检测。通过属性相似性度量，将屋顶数据集分为候选和非候选子集，减少了对象空间中区域生长的范围。实验结果表明，该方法能够鲁棒、高效地提取建筑物屋顶的平面面片。
Planar patches are important primitives for polyhedral building models. One of the key challenges for successful reconstruction of three-dimensional (3D) building models from airborne lidar point clouds is achieving high quality recognition and segmentation of the roof planar points. Unfortunately, the current automatic extraction processes for planar surfaces continue to suffer from limitations such as sensitivity to the selection of seed points and the lack of computational efficiency. In order to address these drawbacks, a new fully automatic segmentation method is proposed in this article, which is capable of the following: (1) processing a roof point dataset with an arbitrary shape; (2) robustly selecting the seed points in a parameter space with reduced dimensions; and (3) segmenting the planar patches in a sub-dataset with similar attributes when region growing in the object space. The detection of seed points in the parameter space was improved by mapping the accumulator array to a 1D space. The range for region growing in the object space was reduced by an attribute similarity measure that split the roof dataset into candidate and non-candidate subsets. The experimental results confirmed that the proposed approach can extract planar patches of building roofs robustly and efficiently.