基于协方差矩阵的点云孔洞边界检测

逆向工程中点云模型孔洞边界的检测是孔洞修补的前提,完美的孔洞轮廓线有利于提高孔洞修补的质量。参照二维图像中边界的定义,给出了三维模型中点云孔洞边界的定义,并且提出了一种简单的孔洞轮廓线生成算法。首先,通过分析邻域点协方差矩阵特征值之间的关系,提出了一种边界点检测算子,用于初步提取孔洞特征点;然后,综合考虑邻域点最大特征向量方向、距离以及边界检测值(边界检测算子)三者之间的关系生成孔洞轮廓线。试验结果表明,所提边界点检测算子能够有效检测模型中的孔洞点,并且有较好的抗噪声性能,生成的孔洞轮廓线比仅以距离为参数的轮廓线生成算法的精度高。

Point cloud hole boundary detection based on covariance matrix

The detection of the hole boundary of the point cloud model in reverse engineering is the prerequisite for hole repair,and the perfect hole contour line is conducive to improving the quality of hole repair.Referring to the definition of the boundary in the two-dimensional image,the definition of the point cloud hole boundary in the three-dimensional model was given,and a simple hole contour line generation algorithm was proposed.Firstly,by analyzing the relationships among the eigenvalues of the neighborhood point covariance matrix,a boundary point detection operator was proposed to extract the feature points of the hole preliminarily.Then the relationship among the direction of the maximum eigenvector of the neighborhood points,the distance,and the boundary detection value(boundary point detection operator)was used to generate the hole contour line.The experimental results show that the proposed boundary point detection operator can effectively detect the hole points in the model and has good antinoise performance.The generated hole contour line is more accurate than the contour line growth algorithm that only uses distance as a parameter.