  This work proposes a novel approach to 4D radar-based scene flow estimation
via cross-modal learning. Our approach is motivated by the co-located sensing
redundancy in modern autonomous vehicles. Such redundancy implicitly provides
various forms of supervision cues to the radar scene flow estimation.
Specifically, we introduce a multi-task model architecture for the identified
cross-modal learning problem and propose loss functions to opportunistically
engage scene flow estimation using multiple cross-modal constraints for
effective model training. Extensive experiments show the state-of-the-art
performance of our method and demonstrate the effectiveness of cross-modal
supervised learning to infer more accurate 4D radar scene flow. We also show
its usefulness to two subtasks - motion segmentation and ego-motion estimation.
Our source code will be available on https://github.com/Toytiny/CMFlow.
