Scene Flow Estimation for Autonomous Driving via Correlation Compensation and Initial Motion Check

Scene flow estimation from LiDAR sensors is a crucial task for dynamic environmental perception in autonomous driving scenarios. Recently, self-supervised approaches have gained attention for their ability to reduce the burden of point-wise annotation. Although existing methods have been able to generate initial flow fields by constructing point-to-point correspondences between adjacent frames of point clouds, the reliability of correlation extraction and initial motion measurement has not been adequately considered. To address this problem, we propose a novel deep neural network to estimate scene flow from LiDAR sensors. Unlike previous works, our approach incorporates a Statistical-based Correlation Compensation Module (SCCM) that leverages statistical features to capture more reasonable correspondences. Furthermore, we design a Holistic Correlation Compensation Module (HCCM) to capture the overall correspondence that reflects most of the rigid motion in dynamic environments. In addition, an Initial Motion Check Mechanism (IMCM) is introduced to calibrate the initial flow and provide more reliable motion priors for subsequent flow refinement. Extensive experimental results on public scene flow benchmarks show that the proposed approach achieves competitive performance in autonomous driving scenarios.