Quasi-Newton Solver for Robust Non-Rigid Registration

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 l_p type robust estimator to regularize the fitting and smoothness, and the proximal operator is used...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 7597 - 7606
Hauptverfasser: Yao, Yuxin, Deng, Bailin, Xu, Weiwei, Zhang, Juyong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2020
Schlagworte:
Convergence
Iterative closest point algorithm
Measurement
Optimization
Robustness
Strain
Three-dimensional displays
ISSN:1063-6919
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Zusammenfassung: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 l_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.
ISSN:1063-6919
DOI:10.1109/CVPR42600.2020.00762