  Probabilistic methods for point set registration have demonstrated
competitive results in recent years. These techniques estimate a probability
distribution model of the point clouds. While such a representation has shown
promise, it is highly sensitive to variations in the density of 3D points. This
fundamental problem is primarily caused by changes in the sensor location
across point sets. We revisit the foundations of the probabilistic registration
paradigm. Contrary to previous works, we model the underlying structure of the
scene as a latent probability distribution, and thereby induce invariance to
point set density changes. Both the probabilistic model of the scene and the
registration parameters are inferred by minimizing the Kullback-Leibler
divergence in an Expectation Maximization based framework. Our density-adaptive
registration successfully handles severe density variations commonly
encountered in terrestrial Lidar applications. We perform extensive experiments
on several challenging real-world Lidar datasets. The results demonstrate that
our approach outperforms state-of-the-art probabilistic methods for multi-view
registration, without the need of re-sampling. Code is available at
https://github.com/felja633/DARE.
