DeepGMR: Learning Latent Gaussian Mixture Models for Registration

Wentao Yuan1
Ben Eckart2
Kihwan Kim2
Varun Jampani2
Dieter Fox1,2
Jan Kautz2
1University of Washington


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.


W. Yuan, B. Eckart, K. Kim, V. Jampani, D. Fox, J. Kautz

DeepGMR: Learning Latent Gaussian Mixture Models for Registration.

ECCV, 2020.

[arXiv]     [Bibtex]

Code and Data

[GitHub]         [Google Drive]