  We present 3DRegNet, a novel deep learning architecture for the registration
of 3D scans. Given a set of 3D point correspondences, we build a deep neural
network to address the following two challenges: (i) classification of the
point correspondences into inliers/outliers, and (ii) regression of the motion
parameters that align the scans into a common reference frame. With regard to
regression, we present two alternative approaches: (i) a Deep Neural Network
(DNN) registration and (ii) a Procrustes approach using SVD to estimate the
transformation. Our correspondence-based approach achieves a higher speedup
compared to competing baselines. We further propose the use of a refinement
network, which consists of a smaller 3DRegNet as a refinement to improve the
accuracy of the registration. Extensive experiments on two challenging datasets
demonstrate that we outperform other methods and achieve state-of-the-art
results. The code is available.
