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arXiv · 2303.00462

Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision

Fangqiang Ding·Andras Palffy·Dariu M. Gavrila·Chris Xiaoxuan Lu
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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.

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Fangqiang Ding, Andras Palffy, Dariu M. Gavrila, Chris Xiaoxuan Lu. 2023-03-01. Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision. https://arxiv.org/abs/2303.00462

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