基于点密度采样的建筑物点云立面分割方法

Building Facade Segmentation Method Based on the Point Density Sampling

由于地面激光扫描仪扫描时常存在死角,导致点云缺失、密度不均匀等问题,使得建筑物立面难以完整分割,为点云后续三维重建带来了很大的困难。提出了一种基于点密度的指导采样方式,并对提取的模型进行再优化的分割算法,即GSMOSAC(global sample and model optimize sampling and consensus)算法。该算法改进了最小采样集的选取方式,并对采样模型进行优化处理,以提高所提取模型的可靠性。针对三种不同类型的激光雷达点云数据的实验结果表明,该算法的分割效果比传统的RANSAC算法和多结构(Multi-GS)算法都更好。

Since terrestrial laser scanner exists scanning corner which may lead to problems such as lac-king of point cloud and uneven density, it is hard to complete segmentation of building facade and brings great difficulty for sequent 3D reconstruction. There exist a lot of algorithms related to building facade segmentation based LiDAR point cloud datas. RANSAC and Mutil-GS have obvious advantage in sampling strategy among these algorithms in the literature, but there exists shortcomings for model selection and subsequent optimiza-tion. Based on a guidance of sampling point density and optimizing the extracted model, this paper puts for-ward a Global Sample and Model Optimization Sampling and Consensus （GSMOSAC） algorithm. Comparing with the traditional RANSAC and Mutil-GS, the algorithm obtains a better segmentation quality in the light of the experiment results under three types of LiDAR point cloud datas.