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 0.5 seconds where other models fail to deliver meaningful results.

设计了一种学习无关的流估计器 ICP-Flow，通过在自动驾驶中包含刚性运动假设来提高目标关联和局部刚性变换估计的性能。在场景流估计中，ICP-Flow 在其他模型无法提供有意义结果的长时间间隔（最高 0.5 秒）内展现出优越性能。