  Probabilistic 3D point cloud registration methods have shown competitive
performance in overcoming noise, outliers, and density variations. However,
registering point cloud pairs in the case of partial overlap is still a
challenge. This paper proposes a novel overlap-guided probabilistic
registration approach that computes the optimal transformation from matched
Gaussian Mixture Model (GMM) parameters. We reformulate the registration
problem as the problem of aligning two Gaussian mixtures such that a
statistical discrepancy measure between the two corresponding mixtures is
minimized. We introduce a Transformer-based detection module to detect
overlapping regions, and represent the input point clouds using GMMs by guiding
their alignment through overlap scores computed by this detection module.
Experiments show that our method achieves superior registration accuracy and
efficiency than state-of-the-art methods when handling point clouds with
partial overlap and different densities on synthetic and real-world datasets.
https://github.com/gfmei/ogmm
