Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework

doi:10.1007/s13042-022-01673-w

Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework

2022-10-30

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DOI：10.1007/s13042-022-01673-w

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In order to establish effective correspondences, a two-phase registration method for three-dimensional point set is proposed under the Bayesian mixture framework. In the first phase, the mixture model consisted of student's t distribution and von Mises-Fisher (vMF) distribution is designed to perform similarity point set registration for recovering rotation transformation, where both distributions are used to measure positional and directional errors, respectively. The second phase implements nonrigid (affine as a particular case) registration between data point set and transformed model point set obtained in the first phase, which is based on student's t mixture model (SMM) using positional information only. In each phase, variational inference is used to obtain approximate posteriors of model parameters. The experimental results on various datasets demonstrate that our proposed method can achieve better registration performance in terms of robustness to rotation and outliers.

Keywords:

Point set registration

Three-dimensional

Bayesian mixture model

Variational inference

Student's t distribution

Von Mises-Fisher (vMF) distribution

Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework

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