  This work addresses the problem of point cloud registration using deep neural
networks. We propose an approach to predict the alignment between two point
clouds with overlapping data content, but displaced origins. Such point clouds
originate, for example, from consecutive measurements of a LiDAR mounted on a
moving platform. The main difficulty in deep registration of raw point clouds
is the fusion of template and source point cloud. Our proposed architecture
applies flow embedding to tackle this problem, which generates features that
describe the motion of each template point. These features are then used to
predict the alignment in an end-to-end fashion without extracting explicit
point correspondences between both input clouds. We rely on the KITTI odometry
and ModelNet40 datasets for evaluating our method on various point
distributions. Our approach achieves state-of-the-art accuracy and the lowest
run-time of the compared methods.
