  Probabilistic methods for point set registration have interesting theoretical
properties, such as linear complexity in the number of used points, and they
easily generalize to joint registration of multiple point sets. In this work,
we improve their recognition performance to match state of the art. This is
done by incorporating learned features, by adding a von Mises-Fisher feature
model in each mixture component, and by using learned attention weights. We
learn these jointly using a registration loss learning strategy (RLL) that
directly uses the registration error as a loss, by back-propagating through the
registration iterations. This is possible as the probabilistic registration is
fully differentiable, and the result is a learning framework that is truly
end-to-end. We perform extensive experiments on the 3DMatch and Kitti datasets.
The experiments demonstrate that our approach benefits significantly from the
integration of the learned features and our learning strategy, outperforming
the state-of-the-art on Kitti. Code is available at
https://github.com/felja633/RLLReg.
