Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models

Abstract

Sampling noise and density variation widely exist in the point cloud acquisition process, leading to few accurate point-to-point correspondences. Since they rely on point-to-point correspondence search, existing state-of-the-art point cloud registration methods face difficulty in overcoming the sampling noise and density variation accurately or efficiently. Moreover, the recent state-of-the-art learning-based methods requires ground-truth transformation as supervised information which lead to large labor costs in real scenes. In this paper, our motivation is that two point-clouds are considered two samples from a unified Gaussian Mixture Model (UGMM). Then, we leverage the advantage of the statistic model to overcome the noise and density variants, and uses the alignment score in the UGMM to supervise the network training. To achieve this motivation, we propose a new unsupervised learning-based probabilistic registration algorithm to reconstruct the unified GMM and solve the registration problem simultaneously. The proposed method formulates the registration problem into a clustering problem, which estimates the posterior probability that classifies the points of two input point clouds to components of the unified GMM. A new feature interaction module is designed to learn the posterior probability using both the self and cross point cloud information. Then, two differential modules are proposed to calculate the GMM parameters and transformation matrices. Experimental results on synthetic and real-world point cloud datasets demonstrate that our unsupervised method achieves better registration accuracy and efficiency than the state-of-the-art supervised and semi-supervised methods in handling noisy and density variant point clouds.