Abstract We propose a new point set registration method based on mixture framework and variational inference. A three-phase registration strategy (TRS) is proposed to automatically process point set registration problem in different cases. A Gaussian variational mixture model (GVMM) with isotropic and anisotropic components under the variational inference framework is designed to weaken the effect of outliers. The Dirichlet distribution is applied to govern the mixture proportion of Gaussian components and then distinguishes missing points. We test the performance of our method in contour registration, Graffiti images, retinal images, remote sensing images and 3D human motion, and compare with six state-of-the-art methods. Our method shows favorable performances in most scenarios.

中文翻译：

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具有混合框架和变分推理的点集注册

摘要 我们提出了一种新的基于混合框架和变分推理的点集注册方法。提出了一种三阶段配准策略（TRS）来自动处理不同情况下的点集配准问题。设计了在变分推理框架下具有各向同性和各向异性分量的高斯变分混合模型（GVMM），以削弱异常值的影响。Dirichlet 分布用于控制高斯分量的混合比例，然后区分缺失点。我们测试了我们的方法在轮廓配准、涂鸦图像、视网膜图像、遥感图像和 3D 人体运动方面的性能，并与六种最先进的方法进行了比较。我们的方法在大多数情况下都显示出良好的性能。