Research On High-Quality Point Cloud Resampling Based On Centroidal Voronoi Tessellation

Point cloud is a collection of large numbers of points in space.With the advent of various scanning tools,we can easily obtain the point cloud data of objects around us nowadays.Whether it is a low-precision point cloud derived from a simple camera by measurement principle or a high-precision point cloud ob-tained from a precision laser radar,the problem of low distribution quality is common.The main reasons are:the undesired spatial relationship between the observation position and the surface to be measured,resulting in uneven distri-bution of points;interference from dynamic objects,resulting in noise;owing to the obstruction,static tools are unable to scan the object completely,which result-s in voids.How to extract the most effective parts which represent the original object well and truly from a raw point cloud is an interesting problem,that has al-ways been difficulty in the initial processing of point cloud.Further research and application also expect high-quality point cloud input.For example:3D object reconstruction,reverse engineering and Finite Element Analysis.Confronting various needs and challenges mentioned above,the entry of this paper has been brought up:the main work of this paper is to start with a known dense point cloud and try to seek a sparse point cloud of a small number to ap-proximate the previous dense point cloud.In this paper,a novel point cloud resampling method is proposed,which is capable of resampling a relatively s-mooth raw dense point cloud.The main theoretical contribution of this paper is to apply the Centroidal Voronoi Tessellation method to the point cloud which is used for mesh before.Firstly,a preliminary random sampling is performed according to the input point cloud.Secondly,the local optimal fitting surface of each sampling point is calculated.Then a restricted Voronoi diagram is calculat-ed according to the sampling points and the local surfaces.Finally,different nu-merical optimization methods are adopted to achieve Centroidal Voronoi Trian-gulation as needed.The output point cloud mainly has the following four char-acteristics:a global uniform distribution,a uniform distribution with weights,an anisotropic distribution and a high norm distribution.A lot of experiments are carried out to show that the method proposed by this paper is not only operated easily,high efficiently,but also able to provide the above versatile kinds of output.The feature of affording more than unifor-m resampling result does not contain in most existing point cloud resampling algorithms.