Graph Matching Optimization Network for Point Cloud Registration
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, 00, pp. 5320-5325
- Issue Date:
- 2023-12-13
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1712985.pdf | Published version | 1.8 MB |
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Point Cloud Registration is a fundamental and challenging problem in 3D computer vision Recent works often utilize geometric structure features in downsampled points patches to seek correspondences then propagate these sparse patch correspondences to the dense level in the corresponding patches neighborhood However they neglect the explicit global scale rigid constraint at the dense level point matching We claim that the explicit isometry preserving constraint in the dense level on a global scale is also important for improving feature representation in the training stage To this end we propose a Graph Matching Optimization based Network GMONet for short which utilizes the graph matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation Specifically we exploit a partial graph matching optimizer to enhance the super point i e down sampled key points features and a full graph matching optimizer to improve the dense level point features in the overlap region Meanwhile we leverage the inexact proximal point method and the mini batch sampling technique to accelerate these two graph matching optimizers Given high discriminative point features in the evaluation stage we utilize the RANSAC approach to estimate the transformation between the scanned pairs The proposed method has been evaluated on the 3DMatch 3DLoMatch and the KITTI datasets The experimental results show that our method performs competitively compared to state of the art baselines
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