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
this https URL

本研究利用基于期望最大化的框架最小化 KL 距离，构建点云场景的潜在概率分布模型，使其能够适应密度变化，并成功在多种真实世界的激光雷达数据集上超越现有的多视角概率方法，而无需重新采样。