Research on denoising of skinned point cloud based on multi-feature point parameter weight optimization

Abstract

The effect of point cloud denoising is very important for the subsequent surface fitting and modeling design of the 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot. However, the key to point cloud denoising lies in singular values and outliers. Therefore, this paper proposes a denoising model coupled with multi-feature parameters, discusses the influence degree of each feature point parameter separately, and uses the swarm intelligence algorithm to solve a set of optimal parameter weights to determine the point cloud denoising model, and to achieve the optimal denoising effect of 3D scattered point cloud. The simulation results show that the swarm intelligence algorithm used is faster and less time-consuming than the existing differential evolution algorithm. At the same time, the point cloud denoising model proposed in this paper has better performance than radius filtering and statistical filtering. denoising effect.