Features extraction of point clouds based on Otsu’s algorithm

Abstract

Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the curvature value of each point cloud is calculated based on the r-neighborhood of the point cloud data. Secondly, the Otsu algorithm is improved by taking the curvature values as input for the maximum inter-class variance method. The optimal segmentation threshold is obtained based on the Otsu algorithm to divide the point cloud data into two parts. Point cloud data whose curvature is greater than or equal to the threshold is extracted as feature point data. In order to verify the reliability of the algorithm presented in this paper, a method for accuracy assessment of regular point cloud data is proposed. Additionally, comparative analysis was conducted on data with varying point cloud densities and on data contaminated with Gaussian white noise using multiple methods. Experimental results show that the proposed algorithm achieves good extraction results for data with 90 percent simplification rate and low noise.