DMRFlow: 4D Radar Scene Flow Estimation With Decoupled Matching and Refinement

Scene flow estimation from 4D radar sensors has become increasingly popular in recent years. In this paper, we propose a matching and refinement decoupling method to estimate scene flow from 4D radar point clouds. Since 4D radar point clouds are much sparser and noisier than LiDAR point clouds, it is challenging to effectively establish correspondences between two frames and properly refine flow fields in the 3D space. To address this issue, we present decoupled correlation fields and decoupled flow fields for scene flow estimation, named DMRFlow. On the one hand, we propose a position-velocity decoupled matching approach that decouples the positional features from the velocity features of two adjacent point clouds and matches them separately. On the other hand, we design a dynamic-static decoupled refinement approach that splits initial flow fields into two groups according to motion segmentation maps and refines them separately. By integrating the matching and refinement decoupling method, our DMRFlow is able to effectively reduce mutual interference between different features during the matching and refinement process. We evaluate the proposed approach on the View-of-Delft (VoD) dataset. Experimental results show that DMRFlow yields competitive performance in autonomous driving scenarios compared to recent 4D radar scene flow estimation methods.