Delaunay growth algorithm based on point cloud curvature smoothing improvement

In order to meet the requirements of 3D reconstruction in accuracy, reconstruction speed and algorithm applicability, this paper proposes a Delaunay growth algorithm based on point cloud curvature smoothing, which firstly projects a 3D discrete point cloud into a 2D plane and passes a 2D Delaunay triangulation. The two-dimensional Delaunay triangulation is performed by the empty circle criterion and the maximum and minimum angle criterion in the score. The PCA principal component analysis is used to estimate the normal of the three-dimensional point cloud and locate the normal on the same side to avoid the disordered points. The cloud normal, combined with the curvature of the corresponding 3D point cloud, removes the invalid normal in the point cloud due to invalid points and preserves the larger part of the point cloud as much as possible, and finally passes the Delaunay constraint criterion and the evaluation function. Filter the set of alternate points to ensure that the reconstructed triangle approximates the Delaunay triangle. The experimental results show that the reconstruction algorithm proposed in this paper is much better than the traditional greedy triangle projection algorithm and Poisson algorithm and the reconstruction speed is increased by 20%.