Quasi-Newton Solver for Robust Non-Rigid Registration

• URL: http://arxiv.org/abs/2004.04322v1
• Date: Thu, 9 Apr 2020 01:45:05 GMT
• Title: Quasi-Newton Solver for Robust Non-Rigid Registration
• Authors: Yuxin Yao, Bailin Deng, Weiwei Xu, Juyong Zhang
• Abstract summary: We propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS.
• Score: 35.66014845211251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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