Point rotation invariant features and attention fusion network for point cloud registration of 3D shapes

Point cloud registration of 3D shapes remains a formidable challenge in computer vision and autonomous driving. This paper introduces a novel learning-based registration method, titled Point Rotation Invariant Feature and Attention Fusion Network (PRIF), specifically tailored for point cloud registr...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 15094 - 16
Main Authors: Liu, Zeyang, Lu, Zhiguo, Shan, Yancong
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 29.04.2025
Nature Portfolio
Subjects:
Feature extraction
Humanities and Social Sciences
multidisciplinary
Neural network
Point cloud registration
Science
Science (multidisciplinary)
Point cloud registration
Feature extraction
Neural network
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Point cloud registration of 3D shapes remains a formidable challenge in computer vision and autonomous driving. This paper introduces a novel learning-based registration method, titled Point Rotation Invariant Feature and Attention Fusion Network (PRIF), specifically tailored for point cloud registration tasks. A rapid and straightforward approach for extracting rotation-invariant information is put forward. Leveraging the strengths of the PointNet+ + structure and attention mechanism, a fresh feature extraction module for point clouds is devised, ensuring efficient feature extraction and matching. Furthermore, a novel feature fusion module is proposed for point cloud registration, facilitating the acquisition of high-quality point pair matching relationships. The network directly ingests raw point clouds and exhibits robust and precise registration capabilities for 3D shapes. The model is trained on the ModelNet40 (Wu et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1912–1920, 2015) dataset and evaluated on both ModelNet40 and ShapeNet (Chang et al. in Shapenet: an information-rich 3d model repository, 2015. arXiv:1512.03012 ) datasets, demonstrating its generalization capabilities. The experimental results show that the method performs well in registration accuracy. Visualization experiments further illustrate the exceptional performance of our network in point cloud registration tasks.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-99240-0