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

Scalable Scene Flow from Point Clouds in the Real World