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Research map / Detection and segmentation / 3D detection and driving

RADIANT: Radar-Image Association Network for 3D Object Detection

AAAI 2023 · Detection and segmentation › 3D detection and driving · approach: Monocular 3D object detection · 46 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add RADIANT radar fusion to PGDPGD · nuScenes testmAP: 0.360 -> 0.380Helped
Add RADIANT radar fusion to PGDPGD · nuScenes testmATE: 0.646 -> 0.609Helped
Add RADIANT radar fusion to FCOS3DFCOS3D · nuScenes validationmAP: 0.326 -> 0.363Helped
Add RADIANT radar fusion to FCOS3DFCOS3D · nuScenes validationmATE: 0.739 -> 0.653Helped
Add depth weighting network (DWN) instead of average fusion (ablation)PGD + RADIANT · nuScenes validationmATE: 0.647 -> 0.617Helped
Add depth weighting network (DWN) instead of average fusion (ablation)PGD + RADIANT · nuScenes validationmAP: 0.371 -> 0.384Helped
Add depth weighting network (DWN) instead of average fusion (ablation)FCOS3D + RADIANT · nuScenes validationmATE: 0.711 -> 0.653Helped
Add depth weighting network (DWN) instead of average fusion (ablation)FCOS3D + RADIANT · nuScenes validationmAP: 0.342 -> 0.363Helped
Add radar-camera depth fusion (ablation)PGD · nuScenes validationmATE: 0.658 -> 0.617Helped
Add radar-camera depth fusion (ablation)PGD · nuScenes validationmAP: 0.368 -> 0.384Helped

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