Simplification algorithm of denture point cloud based on feature preserving

Abstract

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

Fast subsampling strategy for point cloud based on novel octree coding

“…Ji et al [13] utilize an octree structure to spatially partition the point cloud, and then propose a point importance metric formula that considers multiple features, which can effectively retain the detail features. Wang et al [33] use KdTree and octree to spatially partition the feature and nonfeature regions respectively, and then finally merge the detail features with the non-feature regions to obtain a simplified result. He et al propose a multi-feature preserving point cloud subsampling method based on octree partitioning, which can effectively retain the feature points.…”