Point cloud classification method, point cloud segmentation method and related equipment

Abstract

The invention provides a point cloud classification method, a point cloud segmentation method and related equipment. A point cloud classification model and a point cloud segmentation model are constructed based on a graph convolution network structure and a channel attention mechanism; three-dimensional point clouds are classified and segmented by using the constructed point cloud classification model and the constructed point cloud segmentation model; each of the point cloud classification model and the point cloud segmentation model comprises at least one KNN image convolution module and a channel attention module; capturing local features of the point cloud data through the KNN graph convolution module; association between point clouds in the field in the feature space is increased; through the channel attention module, the channel attention of the point cloud data is increased, the interdependence relationship between the feature channels is increased, and the global feature representation of the point cloud is enhanced, so that the prediction accuracy of classification and/or segmentation of the three-dimensional point cloud by using the deep network is improved.

Description

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AI Technical Summary

Benefits of technology

This technology uses graphs with filters or other techniques for extracting relevant attributes from points that are being analyzed by machine learning algorithms such as neural networks (ANN). These models can be used to predict how well different parts of an object look like when they were captured during imagery scans. By comparing these predictions against actual results obtained beforehand, this system helps identify areas where there may have issues affecting its performance. Overall, it allows researchers to accurately analyze large amounts of image data quickly without having them manually inspect each pixel separately.

Problems solved by technology

Prior Art discusses methods used during depth learning when working with three dimensional points (voxels) like Lidar or LiDAR sensors. These techniques require converting them from 2D space into 3D space before performing analysis operations. This conversion requires significant computing resources and may result in slower calculations speeds due to its big size. Additionally, current models are limited because they only work well within specific environments where there's no structural relationships between these objects.

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