Point cloud data segmentation method based on spectral clustering

Abstract

The invention discloses a point cloud data segmentation method based on spectral clustering . The method comprises the following steps of reading an input point cloud data set and a clustering number;normalizing the coordinates of the point cloud data set to obtain normalized coordinates; constructing a similar matrix by normalizing the coordinates, calculating a Laplacian matrix Lrw, and calculating minimum m + 2 eigenvalues and eigenvectors f corresponding to the minimum m + 2 eigenvalues; standardizing a matrix formed by the feature vectors f according to rows to obtain a feature matrix F;taking each row in the feature matrix F as an m + 2-dimensional sample, obtaining m clusters from the samples by using a K-means clustering method, and outputting the m clusters; the method solves aproblem that a conventional point cloud segmentation result is not fine enough, maintains the invariance of the segmentation result for the translation, rotation and zooming of the point cloud data, and is also suitable for point cloud data or sparse data with non-uniform density.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for faster processing without sacrificing accuracy or resolution compared to previous methods like grid models. It converts points clouds from different shapes onto graphs that are easier to analyze than traditional techniques such as histograms. Additionally, it considers both spatially varying characteristics (density) and rotational properties of each vertex's coordinate system. Overall, this method enhances image analysis capabilities while reducing computational time required.

Problems solved by technology

This patents describes different techniques for analyzing three dimensional points (such as laser radar) images from various sources such as aerial photography imagery. These analysis tools have limitations due to factors like complexity of shape recognition patterns, difficulty handling complicated environments containing irregularly shaped targets, high computational requirements, etc., which makes them difficult to use effectively without excessive processing times.

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