| 丁吉祥,董寰宇,秦训鹏.面向结构件几何特征保留的点云精简方法[J].电子测量与仪器学报,2022,36(6):196-204 |
| 面向结构件几何特征保留的点云精简方法 |
| Point cloud simplification method for geometric featurepreservation of structural parts |
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| DOI： |
| 中文关键词: 结构件测量 FCM 聚类 K-means 聚类 特征描述子 点云精简 |
| 英文关键词:measurement of structural parts FCM clustering K-means clustering feature descriptor point cloud simplification |
| 基金项目:湖北省技术创新专项重大项目(2019AAA075)、湖北省技术创新专项重大项目(2020BED010)资助 |
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| 中文摘要: |
| 汽车结构件表面几何特征测量时,传统的点云精简方法在高精简率时会对点云中的几何特征进行破坏,降低几何特征
的完整性和尺寸精度,针对此问题,提出一种面向几何特征保留的点云精简方法。 首先,基于空间区域分割思想进行点云的 Kmeans 聚类划分,并构建几何特征描述子,通过计算簇内信息熵提取特征区域点云。 其次,对特征区域点云进行基于 FCM( fuzzy
C-means)& K-means 的迭代聚类精简,对于非特征区域点云进行八叉树精简。 最后,对不同区域精简后的点云进行拼接,实现
精简的目的。 结果表明,本文方法能较完整地保留模型表面的几何特征,避免孔洞的出现,且在 94. 30%精简率下,精简点云与
原始点云的最大误差为 0. 912 mm,均方根误差为 0. 041 mm,相较于传统的方法精度更高。 |
| 英文摘要: |
| In the surface geometric features measurement of automobile structural parts, the traditional point cloud simplification method
will destroy the geometric features in the point cloud at a high simplification rate, reducing the integrity and dimensional accuracy of
geometric features. To solve this problem, a point cloud simplification method for geometric feature preservation was proposed. Firstly,
the K-means clustering of point clouds was carried out based on the idea of spatial region segmentation, and geometric feature descriptors
were constructed to extract feature region point clouds by calculating information entropy in the cluster. Secondly, the iterative clustering
reduction based on fuzzy C-means (FCM) & K-means was carried out for the point cloud of feature region, and octree reduction was
carried out for the point cloud of non-feature region. Finally, the simplified point clouds of different regions are spliced to achieve the
purpose of simplification. The results show that the proposed method can completely retain the geometric features of the model surface
and avoid the appearance of holes. At the simplification rate of 94. 30%, the maximum error between the simplified point cloud and the
original point cloud is 0. 912 mm, and the root mean square error is 0. 041 mm, which is more accurate than the traditional method. |
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