Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis
<|reference_start|>Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis: This paper presents a novel non-rigid point set registration method that is inspired by unsupervised clustering analysis. Unlike previous approaches that treat the source and target point sets as separate entities, we develop a holistic framework where they are formulated as clustering centroids and clustering members, separately. We then adopt Tikhonov regularization with an $\ell_1$-induced Laplacian kernel instead of the commonly used Gaussian kernel to ensure smooth and more robust displacement fields. Our formulation delivers closed-form solutions, theoretical guarantees, independence from dimensions, and the ability to handle large deformations. Subsequently, we introduce a clustering-improved Nystr\"om method to effectively reduce the computational complexity and storage of the Gram matrix to linear, while providing a rigorous bound for the low-rank approximation. Our method achieves high accuracy results across various scenarios and surpasses competitors by a significant margin, particularly on shapes with substantial deformations. Additionally, we demonstrate the versatility of our method in challenging tasks such as shape transfer and medical registration.<|reference_end|>
@article{zhao2024correspondence-free,
    title={Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised
  Clustering Analysis},
    author={Mingyang Zhao, Jingen Jiang, Lei Ma, Shiqing Xin, Gaofeng Meng,
  Dong-Ming Yan},
    journal={arXiv preprint arXiv:2406.18817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18817},
    primaryClass={cs.CV cs.AI}
}