Point cloud registration method of attention mechanism

By introducing an attention mechanism into point cloud registration, focusing on the feature information of overlapping point cloud regions and utilizing singular value decomposition algorithms, the problem of insufficient point cloud registration accuracy in existing technologies is solved, achieving more efficient point cloud registration and component measurement.

CN116630380BActive Publication Date: 2026-02-13CHENGDU AIRCRAFT INDUSTRY GROUP
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Patent Information

Application Number
CN202310460760.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-04-26
Publication Date
2026-02-13
Estimated Expiration
2043-04-26

AI Technical Summary

Technical Problem

Existing point cloud registration methods struggle to effectively utilize the textual and semantic information of point clouds, resulting in difficulty in improving registration accuracy and impacting the efficiency and accuracy of digital measurement of components in aerospace manufacturing.

Method used

An attention mechanism is adopted, which focuses on the feature information of overlapping areas of point clouds through spatial and channel attention mechanisms, and combined with the singular value decomposition algorithm to improve the accuracy and efficiency of point cloud registration.

Benefits of technology

By focusing on the feature information of overlapping areas in point clouds, the accuracy and efficiency of point cloud registration are significantly improved, thereby increasing the accuracy and speed of digital measurement of parts.

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Abstract

The application belongs to the field of three-dimensional digital measurement, and particularly relates to a point cloud registration method with an attention mechanism, which down-samples input point clouds and target point clouds to facilitate improvement of registration accuracy and efficiency. Spatial and channel attention mechanisms are used to focus on the overlapping area of the two point clouds. The method performs dot product operation on the weighted point cloud features to obtain a correlation matrix corresponding to the points, and then obtains a weighting matrix. The rigid transformation matrix is obtained by singular value decomposition algorithm using the weighting matrix and the two point cloud features, so that the fast and accurate point cloud registration task is realized. The application focuses on the similar areas between the point clouds to be registered by introducing the attention mechanism in deep learning, and extracts the feature information to perform correlation measurement, so as to solve the problem of point cloud registration.
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