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.

翻译：场景流描述了自动驾驶车辆在相邻时间步采集的两帧激光雷达点云之间的三维运动。现有方法通常将场景流视为逐点非约束运动向量，可通过大规模预训练或推理时耗时优化来学习。然而，这类方法未考虑自动驾驶场景中物体常呈现刚性运动这一特性。我们将刚性运动假设融入设计，目标是通过跨帧物体关联来估计局部刚性变换。本文提出ICP-Flow——一种无需学习的流估计器。其核心思想是采用经典迭代最近点（ICP）算法对齐跨帧物体并输出对应刚性变换。关键地，为辅助ICP算法，我们提出基于直方图的初始化方法，通过挖掘最可能的平移量提供良好初始解，进而从刚性变换恢复完整场景流。在Waymo数据集上，我们超越了包括监督模型在内的现有最优基线方法，并在Argoverse-v2和nuScenes上取得具有竞争力的性能。此外，我们训练了由模型生成伪标签监督的前馈神经网络，在具备实时推理能力的模型中达到最优性能。在长达0.5秒的时间间隔场景流估计任务中（此条件下其他模型难以输出有效结果），我们的模型展现出显著优势。