Research on point cloud simplification algorithm for ring forgings based on joint entropy evaluation

Abstract

There are numerous redundant points in the point cloud model of ring forging obtained by 3D laser scanner. How to remove the redundant points while keeping the model characteristics unchanged is a critical issue. This paper proposes a point cloud simplification algorithm based on the joint entropy evaluation theory. Firstly, the K-D tree is used to search for the k-neighbors of the sampled points. Secondly, a surface is fitted to the spatial neighborhood of the sampled points using the least squares method. The curvature operator of the sampled points is derived on the fitted surface using Riemannian geometry theory. After that, an energy operator is defined by using the Euclidean distances of the sampled points and their neighborhood points, and the normal vectors. The joint entropy values of all points in the model are determined based on the probability distributions of these two operators in the local neighborhood. Finally, the data points are sorted by entropy value. Data points with high entropy values are put into the data set U1. Data points with low entropy values are clustered through the K-Means algorithm of swarm optimization. The redundant points outside the cluster centers are removed, and the cluster centers are put into the data set U2. The finalsimplification results are obtained by integratingdata sets U1 and U2. The experimental results show that the point cloud simplification algorithm proposed in this paper is effective and feasible.

A new point cloud simplification algorithm based on V-P container constraint and normal vector angle information entropy

“…Primary point cloud simplification methods were mainly based on grid layering, researchers have realized that the compressed point cloud should retain the points with feature information, which are called key points, this results in a simplified method based on preserving point cloud feature information. Many simplification methods preserve feature point information, by calculating the information entropy of each point [3][4][5][6] or curvature value [7,8] to compress the point cloud. The normal vector angle information entropy and curvature value of each point are the feature information of the point.…”