  We present a novel approach to point set registration which is based on
one-shot adversarial learning. The idea of the algorithm is inspired by recent
successes of generative adversarial networks. Treating the point clouds as
three-dimensional probability distributions, we develop a one-shot adversarial
optimization procedure, in which we train a critic neural network to
distinguish between source and target point sets, while simultaneously learning
the parameters of the transformation to trick the critic into confusing the
points. In contrast to most existing algorithms for point set registration,
ours does not rely on any correspondences between the point clouds. We
demonstrate the performance of the algorithm on several challenging benchmarks
and compare it to the existing baselines.
