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Probabilistic methods for point set registration have interesting theoreticalproperties, such as linear complexity in the number of used points, and theyeasily generalize to joint registration of multiple point sets. In this work,we improve their recognition performance to match state of the art. This isdone by incorporating learned features, by adding a von Mises-Fisher featuremodel in each mixture component, and by using learned attention weights. Welearn these jointly using a registration loss learning strategy (RLL) thatdirectly uses the registration error as a loss, by back-propagating through theregistration iterations. This is possible as the probabilistic registration isfully differentiable, and the result is a learning framework that is trulyend-to-end. We perform extensive experiments on the 3DMatch and Kitti datasets.The experiments demonstrate that our approach benefits significantly from theintegration of the learned features and our learning strategy, outperformingthe state-of-the-art on Kitti. Code is available athttps://github.com/felja633/RLLReg.

Registration Loss Learning for Deep Probabilistic Point Set Registration