Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown
remarkable adaptability in the context of large out-of-distribution autonomous
driving. Despite their success, the underlying reasons for their astonishing
generalization capabilities remain unclear. Our research addresses this gap by
examining the generalization capabilities of NSFP through the lens of uniform
stability, revealing that its performance is inversely proportional to the
number of input point clouds. This finding sheds light on NSFP's effectiveness
in handling large-scale point cloud scene flow estimation tasks. Motivated by
such theoretical insights, we further explore the improvement of scene flow
estimation by leveraging historical point clouds across multiple frames, which
inherently increases the number of point clouds. Consequently, we propose a
simple and effective method for multi-frame point cloud scene flow estimation,
along with a theoretical evaluation of its generalization abilities. Our
analysis confirms that the proposed method maintains a limited generalization
error, suggesting that adding multiple frames to the scene flow optimization
process does not detract from its generalizability. Extensive experimental
results on large-scale autonomous driving Waymo Open and Argoverse lidar
datasets demonstrate that the proposed method achieves state-of-the-art
performance.

通过对大规模点云场景流估计任务的统一稳定性进行理论分析，揭示了 Neural Scene Flow Prior（NSFP）在处理大规模点云场景流估计任务中的有效性。我们进一步探索了利用历史多帧点云改进场景流估计的方法，并在理论和实验结果上验证了其广义性。大规模自动驾驶 Waymo Open 和 Argoverse 激光雷达数据集上的大量实验结果表明，所提出的方法取得了最先进的性能。