DeepGMR: Learning Latent Gaussian Mixture Models for Registration

Understand

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.