  This paper introduces a new method for 3D point cloud registration based on
deep learning. The architecture is composed of three distinct blocs: (i) an
encoder composed of a convolutional graph-based descriptor that encodes the
immediate neighbourhood of each point and an attention mechanism that encodes
the variations of the surface normals. Such descriptors are refined by
highlighting attention between the points of the same set and then between the
points of the two sets. (ii) a matching process that estimates a matrix of
correspondences using the Sinkhorn algorithm. (iii) Finally, the rigid
transformation between the two point clouds is calculated by RANSAC using the
Kc best scores from the correspondence matrix. We conduct experiments on the
ModelNet40 dataset, and our proposed architecture shows very promising results,
outperforming state-of-the-art methods in most of the simulated configurations,
including partial overlap and data augmentation with Gaussian noise.
