基于加权约束的单体建筑物点云表面重建算法

Algorithm based on Weighted Constraints for Reconstructing the Point Cloud Surface of Single-Building

建筑物点云表面重建在高精度城市测绘、虚拟现实等领域有十分广泛的应用前景。由于建筑物的几何形态多变,重建算法普遍存在计算速率慢、拟合精度低和模型结构不完整的问题。为此,本文以单体建筑物为研究对象,提出基于加权约束的单体建筑物点云表面重建算法,在表面初始化过程中充分考虑数据对结构拟合的贡献。在此基础上,构建基于正则集的单体建筑物表面重建算法,实现建筑物拟合过程中的加权拟合误差、近邻结构平滑的同步优化。针对多类建筑物三维点云的实验结果表明,相比传统的建筑物重建策略,本文的加权约束方法可根据不同类型的点云数据设计自适应权重,并选择模型拟合中最优的权重函数,在高噪声、低精度点云数据下能得到更高精度的单体建筑物表面模型。

Building reconstruction based on 3D point cloud data has broad application prospects in fields such as high precision urban mapping and virtual reality. Due to the diverse geometry of buildings, there are widespread problems in traditional reconstruction algorithms, e.g., slow computation speed, low fitting precision, and incompleteness of building structures. Thus, with single-building as the research object, this paper proposed an algorithm based on weighted constraints for reconstructing point cloud surfaces. By fully considering each point’s contribution to the fitting plane during the surface initialization process, the proposed algorithm, which is based on regular sets, simultaneously optimizes the error of adaptively weighted fitting and the smoothness of neighbor structures. The algorithm was applied to the 3D point clouds of various buildings. Results showed that,compared with conventional building reconstruction strategies, the weighted-constraints based algorithm of this study can design adaptive weights according to different types of point clouds, and can choose the optimal weight for model fitting. In cases where the point cloud data contain high noise and low accuracy, the proposed algorithm can help generate more accurate surface models for single-building.