  Autonomous vehicles operate in highly dynamic environments necessitating an
accurate assessment of which aspects of a scene are moving and where they are
moving to. A popular approach to 3D motion estimation, termed scene flow, is to
employ 3D point cloud data from consecutive LiDAR scans, although such
approaches have been limited by the small size of real-world, annotated LiDAR
data. In this work, we introduce a new large-scale dataset for scene flow
estimation derived from corresponding tracked 3D objects, which is
$\sim$1,000$\times$ larger than previous real-world datasets in terms of the
number of annotated frames. We demonstrate how previous works were bounded
based on the amount of real LiDAR data available, suggesting that larger
datasets are required to achieve state-of-the-art predictive performance.
Furthermore, we show how previous heuristics for operating on point clouds such
as down-sampling heavily degrade performance, motivating a new class of models
that are tractable on the full point cloud. To address this issue, we introduce
the FastFlow3D architecture which provides real time inference on the full
point cloud. Additionally, we design human-interpretable metrics that better
capture real world aspects by accounting for ego-motion and providing
breakdowns per object type. We hope that this dataset may provide new
opportunities for developing real world scene flow systems.
