  Learning without supervision how to predict 3D scene flows from point clouds
is essential to many perception systems. We propose a novel learning framework
for this task which improves the necessary regularization. Relying on the
assumption that scene elements are mostly rigid, current smoothness losses are
built on the definition of ``rigid clusters" in the input point clouds. The
definition of these clusters is challenging and has a significant impact on the
quality of predicted flows. We introduce two new consistency losses that
enlarge clusters while preventing them from spreading over distinct objects. In
particular, we enforce \emph{temporal} consistency with a forward-backward
cyclic loss and \emph{spatial} consistency by considering surface orientation
similarity in addition to spatial proximity. The proposed losses are
model-independent and can thus be used in a plug-and-play fashion to
significantly improve the performance of existing models, as demonstrated on
two most widely used architectures. We also showcase the effectiveness and
generalization capability of our framework on four standard sensor-unique
driving datasets, achieving state-of-the-art performance in 3D scene flow
estimation. Our codes are available on https://github.com/ctu-vras/sac-flow.
