  Most of the current scene flow methods choose to model scene flow as a per
point translation vector without differentiating between static and dynamic
components of 3D motion. In this work we present an alternative method for
end-to-end scene flow learning by joint estimation of non-rigid residual flow
and ego-motion flow for dynamic 3D scenes. We propose to learn the relative
rigid transformation from a pair of point clouds followed by an iterative
refinement. We then learn the non-rigid flow from transformed inputs with the
deducted rigid part of the flow. Furthermore, we extend the supervised
framework with self-supervisory signals based on the temporal consistency
property of a point cloud sequence. Our solution allows both training in a
supervised mode complemented by self-supervisory loss terms as well as training
in a fully self-supervised mode. We demonstrate that decomposition of scene
flow into non-rigid flow and ego-motion flow along with an introduction of the
self-supervisory signals allowed us to outperform the current state-of-the-art
supervised methods.
