Abstract: Aiming at the problems of 3D point cloud using traditional Poisson algorithm for grid reconstruction, the reconstruction time is long and the final model has holes and local details missing, a grid optimization algorithm based on point cloud enhancement is proposed. Firstly, the initial point cloud is denoised by statistical filtering. In order to improve the reconstruction efficiency on the basis of guaranteeing the detailed features, the point cloud hole repair is performed using bicubic spline interpolation while performing appropriate point cloud down sampling through voxel filtering. Then, the moving least squares error function is introduced into the point cloud normal calculation to optimize the quality of the point cloud normal vector. The experimental results show that the optimized gridding algorithm takes less time than the traditional Poisson reconstruction algorithm and improves the accuracy of the reconstructed model to a certain extent. 

Key words: 3D point cloud, point cloud enhancement, Poisson reconstruction, moving least squares