  3D point cloud registration is a fundamental problem in computer vision and
robotics. There has been extensive research in this area, but existing methods
meet great challenges in situations with a large proportion of outliers and
time constraints, but without good transformation initialization. Recently, a
series of learning-based algorithms have been introduced and show advantages in
speed. Many of them are based on correspondences between the two point clouds,
so they do not rely on transformation initialization. However, these
learning-based methods are sensitive to outliers, which lead to more incorrect
correspondences. In this paper, we propose a novel deep graph matchingbased
framework for point cloud registration. Specifically, we first transform point
clouds into graphs and extract deep features for each point. Then, we develop a
module based on deep graph matching to calculate a soft correspondence matrix.
By using graph matching, not only the local geometry of each point but also its
structure and topology in a larger range are considered in establishing
correspondences, so that more correct correspondences are found. We train the
network with a loss directly defined on the correspondences, and in the test
stage the soft correspondences are transformed into hard one-to-one
correspondences so that registration can be performed by singular value
decomposition. Furthermore, we introduce a transformer-based method to generate
edges for graph construction, which further improves the quality of the
correspondences. Extensive experiments on registering clean, noisy,
partial-to-partial and unseen category point clouds show that the proposed
method achieves state-of-the-art performance. The code will be made publicly
available at https://github.com/fukexue/RGM.
