  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.
