Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation

ArXi:2605.18507v1 Announce Type: new Due to the difficulty of obtaining ground-truth data for 4D radar scene flow estimation, previous methods typically rely on either self-supervised losses or cross-modal supervision using 3D LiDAR data, 2D images, and odometry. However, self-supervised approaches often yield suboptimal results due to radar's inherently low-fidelity measurements, while existing cross-modal supervised methods