  The proposed RMS-FlowNet is a novel end-to-end learning-based architecture
for accurate and efficient scene flow estimation which can operate on point
clouds of high density. For hierarchical scene flow estimation, the existing
methods depend on either expensive Farthest-Point-Sampling (FPS) or
structure-based scaling which decrease their ability to handle a large number
of points. Unlike these methods, we base our fully supervised architecture on
Random-Sampling (RS) for multiscale scene flow prediction. To this end, we
propose a novel flow embedding design which can predict more robust scene flow
in conjunction with RS. Exhibiting high accuracy, our RMS-FlowNet provides a
faster prediction than state-of-the-art methods and works efficiently on
consecutive dense point clouds of more than 250K points at once. Our
comprehensive experiments verify the accuracy of RMS-FlowNet on the established
FlyingThings3D data set with different point cloud densities and validate our
design choices. Additionally, we show that our model presents a competitive
ability to generalize towards the real-world scenes of KITTI data set without
fine-tuning.
