Density Adaptive Point Set Registration

TL;DR

This paper introduces a density-adaptive probabilistic point set registration method that models scene structure as a latent distribution, achieving invariance to density variations and outperforming existing methods in real-world Lidar datasets.

Contribution

It proposes a novel probabilistic registration framework that models scene structure as a latent distribution, enabling robustness to density changes without re-sampling.

Findings

Outperforms state-of-the-art probabilistic registration methods

Handles severe density variations in terrestrial Lidar data

Does not require re-sampling for different point densities

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 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.