Hole recognition algorithm based on double discriminant criterion in scattered point cloud
Aiming at the problem that the traditional point cloud hole identification algorithm is inefficient and easily affected by sharp points,a hole identification algorithm that combines the semicircle criterion and the maximum angle criterion and introduces eigenvalue constraints is proposed.First,a kdtree index is established to search for neighborhood points,and the semi-disk criterion is used to extract quasi-boundary points.Then the maximum angle criterion is used to remove redundant points to accurately extract hole boundary points.Then the boundary points are screened based on the minimum eigenvalue to eliminate misidentifications.sharp points,and finally cluster multiple hole point sets to complete hole identification.Experimental results show that this algorithm can eliminate the influence of sharp points,identify hole boundary points accurately and efficiently,and lay the foundation for subsequent hole repair operations.
scattered point cloudkdtreethe halfdisc criterionboundary extraction