Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis

• URL: http://arxiv.org/abs/2406.18817v1
• Date: Thu, 27 Jun 2024 01:16:44 GMT
• Title: Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis
• Authors: Mingyang Zhao, Jingen Jiang, Lei Ma, Shiqing Xin, Gaofeng Meng, Dong-Ming Yan,
• Abstract summary: We present a novel non-rigid point set registration method inspired by unsupervised clustering analysis. Our method achieves high accuracy results across various scenarios and surpasses competitors by a significant margin.
• Score: 28.18800845199871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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