With the rapid development of 3D scanning technology in the field of reverse engineering

point clouds that are obtained by 3D scanning devices are commonly dense and disordered.These characteristics of point clouds result in a large number of redundancy data.Moreover

subsequent point processing work

such as smoothing

meshing

and surface reconstruction becomes difficult and inefficient.Therefore

point cloud simplification is a considerably significant prerequisite for smoothing

meshing

surface reconstruction

and other follow-up point cloud processing works.In recent years

point cloud simplification algorithms based on feature preserving method can obtain a better simplified effect than algorithms based on nonselective reduction method presented

which have been widely utilized in several studies.However

the existing algorithms for point cloud simplification still have some inevitable shortcomings

including a large lack of fidelity to the origin

hole generation

and inadaptability to flaky point clouds.This study presents a simplification algorithm of scattered point cloud based on an adaptive K-means clustering

aiming to solve the problems in the existing aforementioned simplified algorithms. The curvature

average vector angle between points

the K-nearest neighborhood points

the distance from the point to its K-nearest neighborhood gravity center

and the average distance from the point to its K-nearest neighborhood points for each data point are calculated.The feature discriminant parameter and feature discriminant threshold are defined based on the four parameters regarded as the most important by the proposed algorithm.A point is recognized as a feature point when its value of discriminant parameter is greater than the threshold value.Thus

the feature extraction based on multiple parameters hybridization method is adopted to identify and preserve these feature points

including surface sharp and boundary points.Then

an adaptive octree is established for the point cloud to allow the K-means to initialize cluster centers and value

which are related to the density distribution of the point cloud.Finally

if the clustering results contain the feature points

then the feature points in the clustering are removed and the cluster centers are updated.In general

cluster members in flat areas are sufficiently similar to each other in the spatial and feature domain.Thus

the cluster center can be employed to represent the cluster.However

in high curvature areas

the cluster members may not be similar to each other in the spatial and feature field because of highly detailed features.Therefore

the cluster will be subdivided to preserve the detail features when the maximum curvature difference between data points in the cluster is greater than the threshold value.The clustering subdivision will continue until the maximum curvature difference is smaller than the threshold value or when only one data point in the cluster is observed.The nearest point to the cluster center is preserved to represent the cluster after the clustering subdivision. In view of clustering

the bunny point cloud is considered as an example for the comparison of traditional K-means clustering algorithm and adaptive K-means clustering algorithm.The comparison result shows that the adaptive K-means clustering algorithm can obtain better data in terms of number of iterations

evaluation function value

and runtime compared with K-means clustering algorithm.In the aspect of simplification

the proposed reduction algorithm is applied to two types of point clouds (i.e.

closed-boundary and flaky point clouds).The experimental results show that when the simplification ratio has the value of 1/5

the reduction errors of the fandisk and saddle models are 0.29×10

-0.41×10

and 0.037

-0.094.Moreover

the boundary shrinkage error of the saddle model is 0.030 805.The aforementioned error values are less than the error values of the grid simplification method and the curvature simplification method. The proposed scattered point cloud simplification algorithm can be used for closed-boundary and flaky point clouds.Moreover

the simplified point cloud is well-distributed in space and can avoid holes.The boundary points of the model can also be protected when the algorithm is applied on the flaky point cloud model.