ICP-Flow: LiDAR Scene Flow Estimation with ICP

Yancong Lin, Holger Caesar; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15501-15511

Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrained flow vectors that can be learned by either large-scale training beforehand or time-consuming optimization at inference. However these methods do not take into account that objects in autonomous driving often move rigidly. We incorporate this rigid-motion assumption into our design where the goal is to associate objects over scans and then estimate the locally rigid transformations. We propose ICP-Flow a learning-free flow estimator. The core of our design is the conventional Iterative Closest Point (ICP) algorithm which aligns the objects over time and outputs the corresponding rigid transformations. Crucially to aid ICP we propose a histogram-based initialization that discovers the most likely translation thus providing a good starting point for ICP. The complete scene flow is then recovered from the rigid transformations. We outperform state-of-the-art baselines including supervised models on the Waymo dataset and perform competitively on Argoverse-v2 and nuScenes. Further we train a feedforward neural network supervised by the pseudo labels from our model and achieve top performance among all models capable of real-time inference. We validate the advantage of our model on scene flow estimation with longer temporal gaps up to 0.4 seconds where other models fail to deliver meaningful results.