Yao, Yuxin, Deng, Bailin  ORCID: https://orcid.org/0000-0002-0158-7670, Xu, Weiwei and Zhang, Juyong
2020.
Quasi-newton solver for robust non-rigid registration.
Presented at: Conference on Computer Vision and Pattern Recognition (CVPR 2020),
Seattle, Washington, USA,
16-18 June 2020.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
IEEE,
pp. 7597-7606.
10.1109/CVPR42600.2020.00762
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Abstract
Imperfect data (noise, outliers and partial overlap) and high degrees of freedom make non-rigid registration a classical challenging problem in computer vision. Existing methods typically adopt the $\ell_{p}$ type robust estimator to regularize the fitting and smoothness, and the proximal operator is used to solve the resulting non-smooth problem. However, the slow convergence of these algorithms limits its wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization, which can handle outliers and partial overlaps. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlap, with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at https://github.com/Juyong/Fast_RNRR.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Publisher: | IEEE |
| ISBN: | 9781728171685 |
| Date of First Compliant Deposit: | 30 March 2020 |
| Date of Acceptance: | 27 February 2020 |
| Last Modified: | 07 Nov 2022 09:56 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/130652 |
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