Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
This addresses scene flow estimation for autonomous vehicles, but is incremental as it builds on existing cross-modal learning approaches.
The paper tackles 4D radar-based scene flow estimation by using cross-modal supervision from co-located sensors in autonomous vehicles, achieving state-of-the-art performance and demonstrating effectiveness for motion segmentation and ego-motion estimation.
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.