  Scene flow estimation is the task of describing the 3D motion field between
temporally successive point clouds. State-of-the-art methods use strong priors
and test-time optimization techniques, but require on the order of tens of
seconds to process full-size point clouds, making them unusable as computer
vision primitives for real-time applications such as open world object
detection. Feedforward methods are considerably faster, running on the order of
tens to hundreds of milliseconds for full-size point clouds, but require
expensive human supervision. To address both limitations, we propose Scene Flow
via Distillation, a simple, scalable distillation framework that uses a
label-free optimization method to produce pseudo-labels to supervise a
feedforward model. Our instantiation of this framework, ZeroFlow, achieves
state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow
Challenge while using zero human labels by simply training on large-scale,
diverse unlabeled data. At test-time, ZeroFlow is over 1000x faster than
label-free state-of-the-art optimization-based methods on full-size point
clouds (34 FPS vs 0.028 FPS) and over 1000x cheaper to train on unlabeled data
compared to the cost of human annotation (\$394 vs ~\$750,000). To facilitate
further research, we release our code, trained model weights, and high quality
pseudo-labels for the Argoverse 2 and Waymo Open datasets at
https://vedder.io/zeroflow.html
