In this paper, we present a new self-supervised scene flow estimation
approach for a pair of consecutive point clouds. The key idea of our approach
is to represent discrete point clouds as continuous probability density
functions using Gaussian mixture models. Scene flow estimation is therefore
converted into the problem of recovering motion from the alignment of
probability density functions, which we achieve using a closed-form expression
of the classic Cauchy-Schwarz divergence. Unlike existing
nearest-neighbor-based approaches that use hard pairwise correspondences, our
proposed approach establishes soft and implicit point correspondences between
point clouds and generates more robust and accurate scene flow in the presence
of missing correspondences and outliers. Comprehensive experiments show that
our method makes noticeable gains over the Chamfer Distance and the Earth
Mover's Distance in real-world environments and achieves state-of-the-art
performance among self-supervised learning methods on FlyingThings3D and KITTI,
even outperforming some supervised methods with ground truth annotations.

本文提出了一种新的自监督场景流估计方法，利用高斯混合模型将离散点云表示为连续的概率密度函数，并使用经典 Cauchy-Schwarz 散度的闭合式表达式从概率密度函数的对齐中恢复运动，通过建立软性隐式点对应关系生成更稳健和准确的场景流，实验结果表明该方法在现实环境中的 FlyingThings3D 和 KITTI 数据集上的性能优于现有方法，甚至超过了一些有监督的方法。