  Point cloud registration is a fundamental problem in 3D computer vision,
graphics and robotics. For the last few decades, existing registration
algorithms have struggled in situations with large transformations, noise, and
time constraints. In this paper, we introduce Deep Gaussian Mixture
Registration (DeepGMR), the first learning-based registration method that
explicitly leverages a probabilistic registration paradigm by formulating
registration as the minimization of KL-divergence between two probability
distributions modeled as mixtures of Gaussians. We design a neural network that
extracts pose-invariant correspondences between raw point clouds and Gaussian
Mixture Model (GMM) parameters and two differentiable compute blocks that
recover the optimal transformation from matched GMM parameters. This
construction allows the network learn an SE(3)-invariant feature space,
producing a global registration method that is real-time, generalizable, and
robust to noise. Across synthetic and real-world data, our proposed method
shows favorable performance when compared with state-of-the-art geometry-based
and learning-based registration methods.
