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 this https URL.

本研究提出了一种新颖的 4D 雷达基于场景流估计的跨模态学习方法，并使用多任务模型体系结构和损失函数，来通过多重跨模态约束有效地进行模型训练，结果表明其在场景流估计上具有良好的表现和实用性。