FilterReg: Robust and Efficient Probabilistic Point-Set Registration
using Gaussian Filter and Twist Parameterization
Abstract
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. Pointset 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 [1] 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). The results demonstrate the proposed method
consistently outperforms many competitive baselines on a
variety of registration tasks. The video demo and source
code are available on our project page.