  Scene flow estimation predicts the 3D motion at each point in successive
LiDAR scans. This detailed, point-level, information can help autonomous
vehicles to accurately predict and understand dynamic changes in their
surroundings. Current state-of-the-art methods require annotated data to train
scene flow networks and the expense of labeling inherently limits their
scalability. Self-supervised approaches can overcome the above limitations, yet
face two principal challenges that hinder optimal performance: point
distribution imbalance and disregard for object-level motion constraints. In
this paper, we propose SeFlow, a self-supervised method that integrates
efficient dynamic classification into a learning-based scene flow pipeline. We
demonstrate that classifying static and dynamic points helps design targeted
objective functions for different motion patterns. We also emphasize the
importance of internal cluster consistency and correct object point association
to refine the scene flow estimation, in particular on object details. Our
real-time capable method achieves state-of-the-art performance on the
self-supervised scene flow task on Argoverse 2 and Waymo datasets. The code is
open-sourced at https://github.com/KTH-RPL/SeFlow along with trained model
weights.
