Abstract
Free-form surface reconstruction using point clouds is a common issue in manufacturing. In this article, a robust joint registration approach for multiview point clouds is proposed to address the problems brought by coarse initialization, outliers, and noise. The basic idea is that minimizing the L2 distance between probability distributions of integrated and standard models, such that a robust initialization is provided for fine registration to avoid local minima. The fine registration is formulated as a joint closet point problem, which is implicitly constrained by closed-loop consistency. In addition, a Lie algebra solution is derived to enforce rigid transformations. The robust initialization is judged by the simulated annealing algorithm. Finally, a probabilistic distance is defined and a maximum likelihood estimation of multiview transformations is designed to resist noise. The experiment on simulated and real data illustrate better robustness of our method to initial errors, outliers, and noise.
Published Version
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