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

Abstract

Abstract 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.