Quasi-Newton Solver for Robust Non-Rigid Registration
This addresses a classical problem in computer vision for applications requiring shape alignment under noisy conditions, but it appears incremental as it builds on existing robust estimators with algorithmic improvements.
The paper tackles the problem of robust non-rigid registration in computer vision, which is challenged by imperfect data like noise and outliers, and proposes a method that outperforms state-of-the-art methods in registration accuracy and computational speed.
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.