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This paper introduces a new method for 3D point cloud registration based ondeep learning. The architecture is composed of three distinct blocs: (i) anencoder composed of a convolutional graph-based descriptor that encodes theimmediate neighbourhood of each point and an attention mechanism that encodesthe variations of the surface normals. Such descriptors are refined byhighlighting attention between the points of the same set and then between thepoints of the two sets. (ii) a matching process that estimates a matrix ofcorrespondences using the Sinkhorn algorithm. (iii) Finally, the rigidtransformation between the two point clouds is calculated by RANSAC using theKc best scores from the correspondence matrix. We conduct experiments on theModelNet40 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.

RoCNet: 3D Robust Registration of Point-Clouds using Deep Learning