A Study of Normal Vector Estimation Method for Three Dimensional Point Cloud

Normal vector estimation is a key link in the data processing of three dimension modeling. By in-depth study of the different normal vector estimation algorithms, an improved normal vector estimation algorithm based on surface fitting is proposed. The surface is fitted by using an improved least square method. The approximate value of normal vector is estimated by minimum eigenvalue of the covariance matrix. The consistency of normal vector is realized by using minimum spanning tree. Euclidean neighborhood, k-nearest neighbor and projection neighborhood are respectively used to test and to analyze the improved algorithm. The experimental results show the effectiveness of the improved algorithm.