A point cloud data reduction method based on curvature
A point cloud data reduction method based on curvature
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DOI：
10.1109/caidcd.2009.5375038
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Xiaolei Du;Yong Zhuo
中科院分区：
文献类型：
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作者：
Xiaolei Du;Yong Zhuo
由于非接触式设备可以高速、准确地采集零件表面数据，因此它成为最流行的零件表面数据采集设备。然而，它创建了大量的点数据，必须减少以减少计算时间和降低存储要求。针对以往点云数据精简方法的局限性，提出了一种基于曲率的点云数据精简方法。包括搜索k-近邻建立数据拓扑结构、计算和调整切平面法线、利用抛物面拟合方法估计曲率以及设定数据精简原则。实验结果表明，新方法在保持几何特征的同时，显著减少了点的数量。
As a non-contact-type device could sample part surface data with high speed and accuracy, it becomes the most popular instrument for capturing the surface data of a part. However, it creates a large amount of point data which must be reduced to decrease computational time and to lower the storage requirement. Aiming at the limitations of point cloud data reduction methods developed in the past, a new reduction method based on curvature is proposed in this paper. It includes searching k-nearest neighbors for constructing data topology, calculating and adjusting tangent plane normal, estimating the curvature by using paraboloid fitting method, and setting the principles of data reduction. The experimental results show that the new method reduces the number of points significantly while preserving the geometry characteristics perfectly.