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Learning without supervision how to predict 3D scene flows from point cloudsis central to many vision systems. We propose a novel learning framework forthis task which improves the necessary regularization. Relying on theassumption that scene elements are mostly rigid, current smoothness losses arebuilt on the definition of ``rigid clusters" in the input point clouds. Thedefinition of these clusters is challenging and has a major impact on thequality of predicted flows. We introduce two new consistency losses thatenlarge clusters while preventing them from spreading over distinct objects. Inparticular, we enforce \emph{temporal} consistency with a forward-backwardcyclic loss and \emph{spatial} consistency by considering surface orientationsimilarity in addition to spatial proximity. The proposed losses aremodel-independent and can thus be used in a plug-and-play fashion tosignificantly improve the performance of existing models, as demonstrated ontwo top-performing ones. We also showcase the effectiveness and generalizationcapability of our framework on four standard sensor-unique driving datasets,achieving state-of-the-art performance in 3D scene flow estimation. Our codesare available anonymously on \url{https://github.com/vacany/sac-flow}.

Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency