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

Adversarial point set registration