Probabilistic point-set registration methods have been gaining more attention
for their robustness to noise, outliers and occlusions. However, these methods
tend to be much slower than the popular iterative closest point (ICP)
algorithms, which severely limits their usability. In this paper, we contribute
a novel probabilistic registration method that achieves state-of-the-art
robustness as well as substantially faster computational performance than
modern ICP implementations. This is achieved using a rigorous yet
computationally-efficient probabilistic formulation. Point-set registration is
cast as a maximum likelihood estimation and solved using the EM algorithm. We
show that with a simple augmentation, the E step can be formulated as a
filtering problem, allowing us to leverage advances in efficient Gaussian
filtering methods. We also propose a customized permutohedral filter for
improved efficiency while retaining sufficient accuracy for our task.
Additionally, we present a simple and efficient twist parameterization that
generalizes our method to the registration of articulated and deformable
objects. For articulated objects, the complexity of our method is almost
independent of the Degrees Of Freedom (DOFs), which makes it highly efficient
even for high DOF systems. The results demonstrate the proposed method
consistently outperforms many competitive baselines on a variety of
registration tasks.

本研究提出了一种新的概率点集配准方法，采用有效的概率公式解决了最大似然估计问题，通过 E-M 算法和高斯滤波方法实现更快的计算性能，适用于关节和可变形目标，优于其他竞争基线。