DeepGMR: Learning Latent Gaussian Mixture Models for Registration

by   Wentao Yuan, et al.

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.


page 1

page 2

page 3

page 4


DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment

Registering accurately point clouds from a cheap low-resolution sensor i...

Rigid and Articulated Point Registration with Expectation Conditional Maximization

This paper addresses the issue of matching rigid and articulated shapes ...

Advancing Mixture Models for Least Squares Optimization

Gaussian mixtures are a powerful and widely used tool to model non-Gauss...

Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models

Matching 3D rigid point clouds in complex environments robustly and accu...

Multiview point cloud registration with anisotropic and space-varying localization noise

In this paper, we address the problem of registering multiple point clou...

DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration

Registration of point clouds related by rigid transformations is one of ...

Deep Global Registration

We present Deep Global Registration, a differentiable framework for pair...

Code Repositories


PyTorch implementation of DeepGMR: Learning Latent Gaussian Mixture Models for Registration (ECCV 2020 spotlight)

view repo