Robust probability model based on variational Bayes for point set registration
作者：
Highlights：
• The point set registration can adapt to a variety of complex situations.
• Asymmetric Generalized Gaussian Mixture Model for Correspondence Evaluation.
• The theory of local variation simplifies the update of complex parameters.
• The global local structure makes the registration more robust.
• Parameter optimization based on variational Bayesian framework.
摘要
•The point set registration can adapt to a variety of complex situations.•Asymmetric Generalized Gaussian Mixture Model for Correspondence Evaluation.•The theory of local variation simplifies the update of complex parameters.•The global local structure makes the registration more robust.•Parameter optimization based on variational Bayesian framework.
论文关键词：Point set registration,Probabilistic model,Asymmetric generalized Gaussian,Local variation,Global–local strategy
论文评审过程：Received 9 August 2021, Revised 13 December 2021, Accepted 6 January 2022, Available online 17 January 2022, Version of Record 1 February 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.108182
