Method and system for improving point cloud classification accuracy based on graph spectral domain

Abstract

The present invention discloses a method and system for enhancing the accuracy of point cloud classification in the spectral domain, relating to the field of point cloud classification. The method comprises the following steps: acquiring original point cloud data of 3D objects; constructing a KNN graph on the original point cloud to represent the geometric structural information, wherein the KNN graph transforms the original point cloud data from the data domain to the spectral domain using GFT; constructing spectral filters to filter the spectral features of the data transformed to the spectral domain, generating perturbed spectral signals; reverting the perturbed spectral signals back to the data domain through GFT, obtaining adversarial point cloud data; generating samples based on the original point cloud data and adversarial samples based on the adversarial point cloud data, serving as training data, and inputting them into the point cloud classification model for classification training; using the trained point cloud classification model to classify the original point cloud data of the target 3D object and producing a classification result. This invention can enhance the accuracy of point cloud classification and recognition by the model.

Dependencies are inferred from the claim text.