Abstract
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 realworld 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