[1]GUO Dawei,LI Jinghao,LU Jun.Multi-scale deep learning method for 3D point cloud registration[J].CAAI Transactions on Intelligent Systems,2024,19(4):817-826.[doi:10.11992/tis.202304007]
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Multi-scale deep learning method for 3D point cloud registration

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 817-826 Column: 学术论文—机器学习 Public date: 2024-07-05
Title:
Multi-scale deep learning method for 3D point cloud registration
Author(s):
GUO Dawei;  LI Jinghao;  LU Jun
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
point cloud registration; 3D vision; feature extraction; feature fusion; deep learning; point of interest aggregation; multi-scale features; Transformer network
CLC:
TP391.4
DOI:
10.11992/tis.202304007
Abstract:
In order to solve the problems of poor robustness and poor accuracy of the point cloud registration algorithm based on deep learning in the field of 3D vision in recent years, a 3D point cloud registration method based on deep learning is designed. Firstly, the points with obvious geometric features are extracted as points of interest, and the points of interest are aggregated by the regional growth algorithm, and feature extraction and feature fusion are carried out based on the multi-scale analysis method. In order to further extract the deep local information contained in the feature data, the multilayer perceptron(MLP) network is used for secondary feature extraction, and the modified Transformer network is added to supplement the features. The matching matrix generation and optimization algorithm is designed, and the transformation matrix is obtained by singular value decomposition(SVD) calculation. Through comparative experiments on the ModelNet40 dataset, it is proved that the registration algorithm in this paper is far superior to the traditional registration algorithm, and is better than the popular deep registration networks DCPNet and RPMNet in recent years in terms of registration accuracy and robustness. The analysis results in this paper provide a reference for improving the robustness and accuracy of point cloud registration.
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