IEEE Trans Pattern Anal Mach Intell. 2017 Feb;39(2):371-384. doi: 10.1109/TPAMI.2016.2545659. Epub 2016 Mar 23.
In this work, we propose a combinative strategy based on regression and clustering for solving point set matching problems under a Bayesian framework, in which the regression estimates the transformation from the model to the sceneand the clustering establishes the correspondence between two point sets. The point set matching model is illustrated by a hierarchical directed graph, and the matching uncertainties are approximated by a coarse-to-fine variational inference algorithm. Furthermore, two Gaussian mixtures are proposed for the estimation of heteroscedastic noise and spurious outliers, and an isotropic or anisotropic covariance can be imposed on each mixture in terms of the transformed model points. The experimental results show that the proposed approach achieves comparable performance to state-of-the-art matching or registration algorithms in terms of both robustness and accuracy.
在这项工作中，我们提出了一种基于回归和聚类的组合策略，用于在贝叶斯框架下解决点集匹配问题，其中回归估计模型到场景的变换，聚类建立两个点集之间的对应关系。点集匹配模型用分层有向图表示，匹配不确定性用粗到细的变分推断算法逼近。此外，还提出了两种高斯混合模型来估计异方差噪声和虚假异常值，并可以根据变换后的模型点为每个混合模型施加各向同性或各向异性协方差。实验结果表明，该方法在鲁棒性和准确性方面都可与最先进的匹配或注册算法相媲美。
