Robust Point Cloud Registration Framework Based on Deep Graph Matching

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 5; pp. 1 - 13
Main Authors: Fu, Kexue, Luo, Jiazheng, Luo, Xiaoyuan, Liu, Shaolei, Zhang, Chenxi, Wang, Manning
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computer vision
Correspondence
Deep learning
Feature extraction
Geometry
Graph Matching
Graph theory
Neural networks
Outliers (statistics)
Point cloud compression
Point Cloud Registration
Prediction algorithms
Registration
Robotics
Three dimensional models
Topology
Transformers
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: https://github.com/fukexue/RGM .
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2022.3204713