UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Driving

LiDAR scene flow is the task of estimating per-point 3D mo-tion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but aretypically only trained and evaluated on a single sensor. In this paper, weaim to learn general motion priors that transfer to diverse and unseenLiDAR sensors. However, prior work in LiDAR semantic segmentationand 3D object detection demonstrate that naively training on multipledatasets yields worse performance than single dataset models. Interest-ingly, we find that this conventional wisdom does not hold for motionestimation, and that state-of-the-art scene flow methods greatly benefitfrom cross-dataset training without architectural modification. We positthat low-level tasks such as motion estimation may be less sensitive tosensor configuration; indeed, our analysis shows that models trained onfast-moving objects (e.g., from highway datasets) perform well on fast-moving objects, even across different datasets. Informed by our analy-sis, we propose UniFlow, a feedforward model that unifies and trainson multiple large-scale LiDAR scene flow datasets with diverse sensorplacements and point cloud densities. Our frustratingly simple solutionestablishes a new state-of-the-art on Waymo and nuScenes, improvingover prior work by 5.1% and 35.2% respectively. Moreover, UniFlowachieves state-of-the-art accuracy on unseen datasets like TruckScenesand AEVAScenes, outperforming prior dataset-specific models by 30.1%and 22.5% respectively. See our project page for additional visuals.