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 to the subsequent surface fitting and modeling design in 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot.However,the key point of point cloud denoising lies in the detection of singular values and outliers. Therefore,a denoising model with coupled multi-feature point parameters is proposed,and the influence of each feature point parameters on the denoising model are discussed respectively. The swarm intelligence algorithm is used to solve a set of optimal parameter weights to determine the point cloud denoising model,so as to achieve the optimal denoising effect of three-dimensional scattered point clouds. The denoising simulation of Bunny model and the denoising experiment of a certain type of skin are used to verify the denoising model. The results show that the point cloud denoising model proposed in this paper has faster iteration,less time-consuming and better denoising effect than that of radius filter,statistical filter and improved voxel filter combined with Gaussian filtering algorithm.

Keywords

• 3d scattered point cloud
• point cloud denoising
• swarm intelligence algorithm
• point cloud feature points