Novel K value optimization method in point cloud clustering denoising process

Abstract

The invention discloses a novel K value optimization method in a point cloud clustering denoising process. The method comprises the steps that (1) a three-dimensional laser scanning instrument obtains space sampling points of the surface of an actual object; (2) the space sampling points are used as K values to optimize a clustering sample for clustering, a K-means clustering method is used for generating different clustering results of point cloud clustering in a clustering number search range, clustering validity indexes are used for evaluating different clustering results, and an obtained best clustering number is used as the optimal K value; (3) the optimal K value is used as a clustering initial value of three-dimensional point cloud clustering denoising, and three-dimensional point clouds are subjected to clustering; and (4) local outlier noise points are identified and removed by carrying out Euclidean-distance-based threshold value judgment in a class of clustering results, and ideal point clouds are obtained. The novel K value optimization method is used, the value is used for carrying out optimization clustering on point clouds with noise, so that the denoising accuracy of ideal point clouds is high, denoising speed is increased, and a later-period reconstructed three-dimensional model is smooth and real.

Description

technical field [0001] The invention belongs to the technical fields of reverse engineering, cluster analysis, etc., and specifically relates to a new K value optimization method in the process of point cloud clustering and denoising. Background technique [0002] Point cloud data generally obtains the three-dimensional geometric coordinates of discrete points on the surface of objects through measuring instruments such as three-dimensional scanners. Due to the limitation of equipment accuracy, the influence of light and the reflection characteristics of materials, these point cloud data containing object three-dimensional coordinate information are inevitable. There are many small-amplitude noises and outliers, and the noisy point cloud data will have a very serious impact on the 3D model reconstructed later. 3D reconstruction has a wide range of applications in the fields of obstacle detection in robot visual navigation, digital protection of cultural relics, urban design ...

AI Technical Summary

Problems solved by technology

This method can identify obvious outlier noise points to a certain extent, but the point cloud generated by this method is not the desired ideal point cloud, so the problem of low denoising accuracy and slow denoising speed will occur.

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