Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
Abstract
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.
Cite
@article{arxiv.2303.00462,
title = {Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision},
author = {Fangqiang Ding and Andras Palffy and Dariu M. Gavrila and Chris Xiaoxuan Lu},
journal= {arXiv preprint arXiv:2303.00462},
year = {2023}
}
Comments
10 pages, 7 figures. Accepted by CVPR 2023. See our code at https://github.com/Toytiny/CMFlow. Supplementary materials can be found at https://drive.google.com/file/d/1Iewcqnjzecge2ePBM8k2tg-85LX5xs3N/view