  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.
