Additional value-added conditional moving least squares method for point cloud hole repair

Due to the limitations of scanning equipment or the complexity of the model structure, holes appear in the point cloud model, which seriously affects the subsequent processing of the model. To address the problem of point cloud hole repair, this paper proposes a point cloud hole repair method with an additional value-added conditional moving least squares method. Firstly, the closed hole boundary is extracted and iteratively sliced through density analysis, which not only reduces the impact of the uneven distribution of the point cloud, but also improves the retention standard of detailed features of the model; Besides, the discrete group of points is projected onto the fitted surface, and the projected point set is fitted twice to obtain the nodes of the fitted surface to ensure that there are enough boundary neighborhood nodes for hole repair; Finally, the holes are repaired by using the additional value-added conditional moving least squares method. Meanwhile, the curvature of the value-added point cloud is constrained, so as to achieve the reconstruction that fits the spatial characteristics of the original model. In the test, different types of holes are artificially made on four point cloud models, and the effectiveness of proposed method is verified by comparison with the existing four methods. The results show that, compared with the four existing methods, the completeness and accuracy of this method are improved by more than 1.83%, and the root mean square error of the alignment and the root mean square of the curvature are reduced by more than 68%, which proves the applicability of this method for point cloud model holes, which can provide reliable information for the reconstruction of 3D point cloud models.