Rethinking Radar 3D Perception: Representation Learning from Raw Spectra
Overview
This paper challenges the assumption that raw radar spectra are too noisy for direct learning. The authors introduce Leakage-aware Spectral MAE, a masked autoencoder that uses corruption strategies inspired by spectral leakage to separate structured signal from noise without handcrafted reductions. By treating Doppler as a primary spatial dimension in an x-y-D representation, the method outperforms state-of-the-art radar-based detectors and point-cloud-based self-supervised learning counterparts on 3D perception tasks.
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- arXiv Robotics — research abstractsSignalRethinking Radar 3D Perception: Representation Learning from Raw Spectra
This paper challenges the assumption that raw radar spectra are too noisy for direct learning. The authors introduce Leakage-aware Spectral MAE, a masked autoencoder that uses corruption strategies inspired by spectral leakage to separate structured signal from noise without handcrafted reductions. By treating Doppler as a primary spatial dimension in an x-y-D representation, the method outperforms state-of-the-art radar-based detectors and point-cloud-based self-supervised learning counterparts on 3D perception tasks.
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