Improved Poisson Reconstruction Algorithm Based on Vector Field and Isosurface

We propose an improved Poisson reconstruction algorithm based on a vector field and an isosurface to improve the precision and accuracy associated with point cloud surface reconstruction. The proposed algorithm intends to solve the following problems: the Poisson reconstruction algorithm misconnects the empty regions and different normal directions cause the deviation of the reconstruction results. Initially, a statistical filter was used to denoise the noisy point cloud data. Subsequently, the weighted principal component analysis method was used to estimate the normal direction, and the moving least squares (MLS) method was used to calculate and optimize the measurement error associated with the point cloud normal. Further, OpenMP was used for accelerating the proposed method. Finally, the improved dual contouring algorithm was used for extracting the isosurface to eliminate the problems of surface empty regions and misconnected surface features. The experimental results demonstrate that the improved Poisson algorithm can effectively eliminate the possible empty regions and pseudoenclosed surfaces in the model and improve the accuracy as well as efficiency of the surface reconstruction.