Miao Zhang

Radar 3D perception has long compressed raw spectra into sparse point clouds or compact Doppler statistics before supervised training. L-SPMAE instead uses masked self-supervision: whatever remains reconstructable under localized corruption counts as signal, and spectral leakage supplies that corruption, so pretraining runs directly on unreduced radar spectra. On K-Radar, L-SPMAE with the RAD representation reaches 55.9/50.1 mAP after fine-tuning, above the best point-cloud self-supervised baseline in the table at 50.8/45.0.

Radar 3D perception has long compressed raw spectra into sparse point clouds or compact Doppler statistics before supervised training. L-SPMAE instead uses masked self-supervision: whatever remains reconstructable under localized corruption counts as signal, and spectral leakage supplies that corruption, so pretraining runs directly on unreduced radar spectra. On K-Radar, L-SPMAE with the RAD representation reaches 55.9/50.1 mAP after fine-tuning, above the best point-cloud self-supervised baseline in the table at 50.8/45.0.