HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds

• URL: http://arxiv.org/abs/2602.11554v2
• Date: Fri, 13 Feb 2026 21:13:04 GMT
• Title: HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
• Authors: Yichun Xiao, Runwei Guan, Fangqiang Ding,
• Abstract summary: We present HyperDet, a detector-agnostic radar-only 3D detection framework.<n>It constructs a task-aware hyper 4D radar point cloud for standard LiDAR-oriented detectors.<n>On MAN TruckScenes, HyperDet consistently improves over raw radar inputs with VoxelNeXt and CenterPoint.
• Score: 7.899148878601621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 4D mmWave radar provides weather-robust, velocity-aware measurements and is more cost-effective than LiDAR. However, radar-only 3D detection still trails LiDAR-based systems because radar point clouds are sparse, irregular, and often corrupted by multipath noise, yielding weak and unstable geometry. We present HyperDet, a detector-agnostic radar-only 3D detection framework that constructs a task-aware hyper 4D radar point cloud for standard LiDAR-oriented detectors. HyperDet aggregates returns from multiple surround-view 4D radars over consecutive frames to improve coverage and density, then applies geometry-aware cross-sensor consensus validation with a lightweight self-consistency check outside overlap regions to suppress inconsistent returns. It further integrates a foreground-focused diffusion module with training-time mixed radar-LiDAR supervision to densify object structures while lifting radar attributes (e.g., Doppler, RCS); the model is distilled into a consistency model for single-step inference. On MAN TruckScenes, HyperDet consistently improves over raw radar inputs with VoxelNeXt and CenterPoint, partially narrowing the radar-LiDAR gap. These results show that input-level refinement enables radar to better leverage LiDAR-oriented detectors without architectural modifications.

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