Local point cloud hole completion method for highly reflective objects
To solve the problem of the traditional greedy projection triangulation algorithm failing to complete point cloud due to significant noise and oversized holes in point cloud data of highly reflective objects,an improved greedy triangulation point cloud completion algorithm was proposed.Statistical filtering and Gaussian filtering were used for outlier removal and smoothing process,while the moving least squares algorithm was utilized to upsample and enhance the local point cloud data to further smooth the point cloud.The k-dimensional tree algorithm in the greedy triangulation algorithm was replaced by a more efficient octree search algorithm,and the principal component analysis was replaced by moving least squares algorithm with higher accuracy for normal estimation.Finally,point cloud triangulation was performed to complete point cloud completion.The experimental results show that the improved algorithm can better complete the holes,resulting in a smoother surface,more accurate structure,and less processing time.
point cloud completionhighly reflective objectsgreedy projection triangulation
万方数据
维普
