Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.

翻译：在计算机图形中,我们的技术基于一个新的神经网络设计,它学习了某种形状之前的形状,可以完成部分形状。关键的想法是将登记和完成任务结合起来,这样可以相互加强。特别是,我们同时用两个连接的流量来培训登记网和完成网络,一个是登记和完成,一个是完整和完整,一个是完整和完整,鼓励两个流动产生一个一致的结果。我们显示,与每个单独的流量相比,这种两流培训导致一个强大和可靠的远程登记,因此也显示一个更好的网络的完成率。