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# 3D object detection

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  1. arXiv

    Raw radar spectra skip the point-cloud step: L-SPMAE pretraining lets a generic detector beat radar-specific detectors on K-Radar

    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.

    Miao Zhang（First author）
    Spectral MAELeakage-aware Spectral MAEMasked AutoencoderSelf-Supervised Learning3D object detection
  2. researchsquare.com

    When rain and blur degrade the camera, the fusion layer learns to trust it less: 1.41 mAP gain on nuScenes rain

    Camera–LiDAR bird's-eye-view fusion can mistake corrupted visual texture for useful evidence when the camera stream degrades. DARFusion recasts fusion as modality trust estimation: learned reliability cues calibrate both feature streams, and calibrated features, task-driven contexts and a compact degradation descriptor derived from camera BEV features decide how much camera evidence to use. It holds 68.4 mAP and 71.5 NDS on the clean nuScenes validation set and beats the reproduced BEVFusion baseline by 1.41 and 1.31 mAP on real rain and night subsets.

    North China Electric Power University
    Weifeng Xu（First author）Hongtao Wang（Corresponding author）
    DARFusionCamera–LiDAR BEV fusionModality trust estimation3D object detection

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