HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
arXiv:2602.11554v4 Announce Type: replace Abstract: How far can 3D object detection go using 4D radar alone? Despite offering weather-robust and velocity-aware sensing for autonomous perception, modern 4D radar still yields sparse, noisy, and unstable point clouds, limiting radar-only 3D detection. We present HyperDet, a detector-agnostic frame- work that constructs task-aware hyper 4D radar point clouds before detection. HyperDet first refines short-window surround-view radar observations thro
Overview
arXiv:2602.11554v4 Announce Type: replace Abstract: How far can 3D object detection go using 4D radar alone? Despite offering weather-robust and velocity-aware sensing for autonomous perception, modern 4D radar still yields sparse, noisy, and unstable point clouds, limiting radar-only 3D detection. We present HyperDet, a detector-agnostic frame- work that constructs task-aware hyper 4D radar point clouds before detection. HyperDet first refines short-window surround-view radar observations through spatio-temporal accumulation, cross-sensor validation, and Doppler-guided mo- tion compensation, improving return reliability and temporal coherence. It then performs foreground generative enhancement using LiDAR-guided pseudo-radar supervision available only during training, enriching object geometry while pre- serving measured radar background and radar-native attributes. During detector training, radar-aware object-level augmentation further preserves Doppler consis- tency under geometric relocation. At inference time, HyperDet requires radar input alone and can be directly paired with standard 3D detectors. Experiments on two public surround-view 4D radar datasets demonstrate consistent improve- ments over raw radar inputs across standard 3D detectors, validating input-level radar enhancement as an effective approach to radar-only 3D detection.
Source
Originally published at arxiv.org.
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Source: https://arxiv.org/abs/2602.11554
