TNList, Department of Computer Science and Technology, Tsinghua University, Room 3-530, FIT Building, Haidian District, Beijing 100084, P.R. China.
IEEE Trans Vis Comput Graph. 2013 Oct;19(10):1700-7. doi: 10.1109/TVCG.2013.74.
The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in IR³ into a set of simple circle detection problems in IR². Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.
从管道工厂扫描的大量点使得工厂重建变得非常困难。由于计算复杂度高，传统的圆柱检测方法无法直接应用。在本文中，我们探索了管道工厂点云中的结构特征，并定义了一个结构特征。基于该结构特征，我们提出了一种分层结构检测和分解方法，将 IR³ 中的复杂管道工厂重建问题转化为一组在 IR² 中简单的圆检测问题。通过工业应用的实验，证明了所提出的结构检测方法的有效性。
