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Point cloud registration is a fundamental problem in computer vision and robotics, and has been widely used in various applications, including 3D reconstruction, simultaneous localization and mapping, and autonomous driving. Over the last decades, numerous researchers have devoted themselves to tackling this challenging problem. With the success of deep learning in geometric vision tasks, various types of deep learning-based point cloud registration methods have been proposed, and some of them exhibit good performance. However, their performance degrades rapidly when the overlap between point clouds is reduced. In this paper, we propose a shape completion-guided registration network for face point cloud registration, which takes face shape as a prior knowledge to guide the network to learn a complete face features at the low overlap. Another noteworthy point is that we use the Regularized Projective Manifold Gradient (RPMG) layer to bridge the gap between the neural network output measured in Euclidean space and the rotation parameters described in the SO(3) manifold. In addition, our proposed method infers the translation parameters from the two complementary point clouds and the predicted rotation rather than estimates them directly. For the problem of low overlap point cloud registration, our method can effectively improve the registration accuracy compared to the current state-of-the-art methods. The rotation error and translation error are, respectively, less than 2 degrees and 2mm on the FaceScape-based low overlap face registration dataset.
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