Abstract
The registration of 3D point cloud with numerous applications in robotics, medical imaging and other industries. However, due to the lack of accurate data annotation, the performance of unsupervised point cloud registration networks is often unsatisfactory. In this paper, we propose an unsupervised method based on generating corresponding points and utilizing structural constraints for rigid point cloud registration. The key components in our approach are similarity optimization module and structure variation checking module. In the similarity optimization module, we improve the similarity matrix by adaptively weighting the matching scores of neighbors. Through this method, the spatial information of matching point pairs can be fully utilized, resulting in high-quality corresponding estimations. We observe that predicted point cloud is crucial for constructing accurate correspondences. Therefore, we developed a structure variation checking module to constrain the predicted point cloud and the source point cloud to have similar structural information. Based on the constraints, the extraction network is continuously optimized and adjusted to obtain even better features. The extensive experimental results show that our method achieves state-of-the-art performance when compared with other supervised and unsupervised tasks on the ModelNet40 data set, and significantly outperforms previous methods on the real-world indoor 7Scenes data set.
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Acknowledgements
This work was supported by national natural science foundation of China (No. 62202346), Hubei key research and development program (No. 2021BAA042), open project of engineering research center of Hubei province for clothing information (No. 2022HBCI01), Wuhan applied basic frontier research project (No. 2022013988065212).
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Yu, F. et al. (2024). AMCNet: Adaptive Matching Constraint for Unsupervised Point Cloud Registration. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_6
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