A point cloud segmentation method based on feature bias value and attention mechanism

The point cloud segmentation method using feature bias values ​​and attention mechanisms solves the problems of insufficient local feature extraction and feature loss in point cloud classification, achieving more efficient point cloud scene segmentation, especially improving accuracy in complex scenes and small-scale objects.

CN116958956BActive Publication Date: 2026-05-29NANJING FORESTRY UNIV
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Patent Information

Application Number
CN202310374323.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-04-10
Publication Date
2026-05-29
Estimated Expiration
2043-04-10

AI Technical Summary

Technical Problem

Existing technologies suffer from insufficient local feature extraction and feature loss in point cloud classification, especially when dealing with irregular and disordered point cloud data, making it difficult to effectively utilize convolutional neural networks.

Method used

A point cloud segmentation method based on feature bias and attention mechanism is adopted. The method uses the farthest sampling method to downsample, the KNN algorithm to extract feature points, calculates the feature bias value of the spherical neighborhood, and uses residual multilayer perceptron and attention pooling to fuse local features to form global features.

Benefits of technology

It improves the performance of point cloud classification, especially enhancing the model's segmentation ability in complex scenes, improving the average accuracy and segmentation accuracy of small-scale objects, reducing feature loss, and improving the model's scene segmentation ability.

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Abstract

The application discloses a point cloud segmentation method based on feature deviation values and an attention mechanism, relates to the technical field of three-dimensional point cloud classification, and utilizes the farthest sampling method to perform down-sampling on point clouds; a threshold radius is set with a sampling point as a center to extract feature points in the range by using a KNN algorithm; after the feature points are extracted, feature deviation values are calculated for each spherical neighborhood with the sampling center point, and then local features of the spherical neighborhood are obtained; residual multilayer perceptron shared weight parameters are utilized to fuse the local features by using attention pooling, and then global features of the point cloud are obtained; and the point cloud is segmented by utilizing the local features and the global features of the spliced point cloud. The application can effectively extract deep geometric features of the point cloud in a complex point cloud scene, fully excavate semantic information of the point cloud, avoid the problem of feature loss in a scene segmentation task, improve the segmentation capability of the model in a complex scene, effectively excavate deep information, and effectively realize scene segmentation of the point cloud.
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