Current methods for self-supervised LiDAR scene flow estimation work poorly
on real data. A variety of flaws in common evaluation protocols have caused
leading approaches to focus on problems that do not exist in real data. We
analyze a suite of recent works and find that despite their focus on deep
learning, the main challenges of the LiDAR scene flow problem -- removing the
dominant rigid motion and robustly estimating the simple motions that remain --
can be more effectively solved with classical techniques such as ICP motion
compensation and enforcing piecewise rigid assumptions. We combine these steps
with a test-time optimization method to form a state-of-the-art system that
does not require any training data. Because our final approach is dataless, it
can be applied on different datasets with diverse LiDAR rigs without
retraining. Our proposed approach outperforms all existing methods on Argoverse
2.0, halves the error rate on NuScenes, and even rivals the performance of
supervised networks on Waymo and lidarKITTI.

通过应用 ICP 运动补偿和分段刚性假设约束，结合测试时间优化方法，自监督 LiDAR 场景流估计技术可以更加有效地解决主要挑战，而不需要训练数据，提升了真实数据上的性能。