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About this paper
Fangqiang Ding, Zhijun Pan, Yimin Deng, Jianning Deng, Chris Xiaoxuan Lu
RaFlow introduces the first self-supervised scene flow estimation method tailored for 4-D automotive radar, addressing the sensor's sparse and noisy data characteristics. The proposed architecture, utilizing unique radial velocity measurements and specialized loss functions, achieved an Avg. RNE of 0.240m on an in-house dataset, outperforming existing baselines by a large margin and demonstrating effective motion segmentation.
Key insights
- Radial Relative Velocity (RRV) measurements from 4-D radar provide a strong intrinsic motion cue, essential for robust self-supervised scene flow estimation.
- Customizing loss functions, such as a Soft Chamfer Loss that tolerates small discrepancies and discards outliers, is vital for handling the inherent sparsity and noise of radar point clouds.
- A dedicated Static Flow Refinement (SFR) module, leveraging rigid ego-motion and RRV, effectively improves the accuracy of scene flow for static points and assists in motion segmentation.
Summary by alphaXiv.
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