  Current scene flow methods broadly fail to describe motion on small objects,
and current scene flow evaluation protocols hide this failure by averaging over
many points, with most drawn larger objects. To fix this evaluation failure, we
propose a new evaluation protocol, Bucket Normalized EPE, which is class-aware
and speed-normalized, enabling contextualized error comparisons between object
types that move at vastly different speeds. To highlight current method
failures, we propose a frustratingly simple supervised scene flow baseline,
TrackFlow, built by bolting a high-quality pretrained detector (trained using
many class rebalancing techniques) onto a simple tracker, that produces
state-of-the-art performance on current standard evaluations and large
improvements over prior art on our new evaluation. Our results make it clear
that all scene flow evaluations must be class and speed aware, and supervised
scene flow methods must address point class imbalances. We release the
evaluation code publicly at
https://github.com/kylevedder/BucketedSceneFlowEval.
