3D Point Cloud Simplification Based on k -Nearest Neighbor and Clustering

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.

Section: Related Workmentioning

confidence: 99%

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

Zhang,

Wu,

et al. 2023

“…The normal vector angle information entropy and curvature value of each point are the feature information of the point. Thus, simplification methods [9][10][11] based on point cloud clustering has gradually emerged, and point cloud clustering has become a new research direction for point cloud simplification. Grid, curvature, information entropy, and clustering methods are the four important methods for simplifying a point cloud.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu,

Li,

Liu

et al. 2024

Most point cloud simplification algorithms use k-order neighborhood parameters, which are set by human experience; thus, the accuracy of point feature information is not high, and each point is repeatedly calculated simultaneously. The proposed method avoids this problem. The first ordinal point of the original point cloud file was used as the starting point, and the same spatial domain was then described. The design method filters out points located in the same spatial domain and stores them in the same V-P container. The normal vector angle information entropy was calculated for each point in each container. Points with information entropy values that met the threshold requirements were extracted and stored as simplified points and new seed points. In the second operation, a point from the seed point set was selected as the starting point for the operation. The same process was repeated as the first operation. After the operation, the point from the seed point set was deleted. This process was repeated until the seed point set was empty and the algorithm ended. The simplified point set thus obtained was the simplified result. Five experimental datasets were selected and compared using the five advanced methods. The results indicate that the proposed method maintains a simplification rate of over 82% and reduces the maximum error, average error, and Hausdorff distance by 0.1099, 0.074, and 0.0062 (the highest values among the five datasets), respectively. This method has superior performance for single object and multi object point cloud sets, particularly as a reference for the study of simplified algorithms for more complex, multi object and ultra-large point cloud sets obtained using terrestrial laser scanning and mobile laser scanning.

“…Shi et al [15] utilize the maximum normal vector deviation as a measure of cluster scatter to partition the gathered point sets into a series of sub-clusters in the feature field. Mahdaoui and Sbai [16] divide the 3D point cloud into clusters using the k-means algorithm. An entropy estimation is performed for each cluster to remove the ones that have minimal entropy.…”

Section: Introductionmentioning

confidence: 99%

A novel point cloud simplification method using local conditional information

Cheng¹,

Li²,

Jiang³

et al. 2022

In three-dimensional measurement of large-scale parts, such as car bodies and aircraft wings, massive points (up to hundreds of millions) are collected. If all point cloud data is processed, a large amount of computing resources and storage space will be consumed. It is an important task to reduce the burden of processing while maintaining the feature of the point cloud as much as possible. This paper proposes a novel point cloud simplification method using local conditional information that is utilized to evaluate the features of each point in point clouds. The proposed method can reduce the size of point clouds while reserving significant features. Firstly, the original information of each point is evaluated by calculating the curvature of the original point cloud. Secondly, the point with the maximal information is selected and reserved in the target point cloud that will be updated as the alternative point cloud after simplification. The conditional information of each rest point is evaluated by the neighborhood and updated to the information of the current point, which attenuates with the distance from the other reserved points. Finally, the point with the maximal information is reserved iteratively until the number of points reserved satisfies the requirements of simplification according to enterprise needs. Experiments verify the effectiveness and performance of the proposed method. The experimental results indicate that the proposed method performs much better on feature retention than Voxel simplification and Curvature simplification.