[1]LU Jun,WANG Wenhao,DU Hongjin.Point cloud registration based on feature fusion and network sampling[J].CAAI Transactions on Intelligent Systems,2025,20(3):621-630.[doi:10.11992/tis.202403022]
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Point cloud registration based on feature fusion and network sampling

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 621-630 Column: 学术论文—机器感知与模式识别 Public date: 2025-05-05
Title:
Point cloud registration based on feature fusion and network sampling
Author(s):
LU Jun;  WANG Wenhao;  DU Hongjin
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
point cloud registration; feature fusion; deep learning; network sampling; 3D vision; local feature; global feature; feature extraction
CLC:
TP391.4
DOI:
10.11992/tis.202403022
Abstract:
To solve the issue of easily losing key points during lower sampling, which affects the registration accuracy during point cloud registration, a registration method is proposed based on network sampling and feature fusion, and this method improves registration accuracy and speed. Based on the PointNet classification network, we design a deep learning (DL) network-based method for key point extraction. The method fuses local features with global features to obtain the feature matrix with fixed characteristics and uses DL to automatically optimize the parameters when calculating the corresponding matrix. Finally, we use weighted singular value decomposition to obtain the transformation matrix and complete the registration. Our experiments using the ModelNet40 dataset reveal that the time consumed for the process is reduced by 45.36% compared with that consumed by farthest point sampling. Compared with the RPM-Net algorithm, the mean square errors of the translation and rotation matrices obtained by the proposed method are reduced by 5.67% and 13.1%, respectively. Further, the designed model was subjected to experiments, which proved the effectiveness of the algorithm in registering real objects.
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