In contrast to current state-of-the-art methods, such as NSFP [25], which
employ deep implicit neural functions for modeling scene flow, we present a
novel approach that utilizes classical kernel representations. This
representation enables our approach to effectively handle dense lidar points
while demonstrating exceptional computational efficiency -- compared to recent
deep approaches -- achieved through the solution of a linear system. As a
runtime optimization-based method, our model exhibits impressive
generalizability across various out-of-distribution scenarios, achieving
competitive performance on large-scale lidar datasets. We propose a new
positional encoding-based kernel that demonstrates state-of-the-art performance
in efficient lidar scene flow estimation on large-scale point clouds. An
important highlight of our method is its near real-time performance (~150-170
ms) with dense lidar data (~8k-144k points), enabling a variety of practical
applications in robotics and autonomous driving scenarios.

我们提出了一种利用经典核表示法的新方法，通过解决线性系统来实现对密集激光雷达点的高效处理，在各种分布场景下具有出色的实时性能和竞争性能，适用于大规模激光雷达数据的高效场景流估计。