  Scene flow estimation is a long-standing problem in computer vision, where
the goal is to find the 3D motion of a scene from its consecutive observations.
Recently, there have been efforts to compute the scene flow from 3D point
clouds. A common approach is to train a regression model that consumes source
and target point clouds and outputs the per-point translation vector. An
alternative is to learn point matches between the point clouds concurrently
with regressing a refinement of the initial correspondence flow. In both cases,
the learning task is very challenging since the flow regression is done in the
free 3D space, and a typical solution is to resort to a large annotated
synthetic dataset. We introduce SCOOP, a new method for scene flow estimation
that can be learned on a small amount of data without employing ground-truth
flow supervision. In contrast to previous work, we train a pure correspondence
model focused on learning point feature representation and initialize the flow
as the difference between a source point and its softly corresponding target
point. Then, in the run-time phase, we directly optimize a flow refinement
component with a self-supervised objective, which leads to a coherent and
accurate flow field between the point clouds. Experiments on widespread
datasets demonstrate the performance gains achieved by our method compared to
existing leading techniques while using a fraction of the training data. Our
code is publicly available at https://github.com/itailang/SCOOP.
