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.

翻译：四维毫米波雷达能够提供天气鲁棒且具备速度感知能力的测量数据，其成本效益也优于激光雷达。然而，纯雷达三维检测的性能仍落后于基于激光雷达的系统，这是因为雷达点云具有稀疏性、不规则性，且常受多径噪声干扰，导致几何信息弱且不稳定。本文提出HyperDet，一个与检测器无关的纯雷达三维检测框架，该框架为标准的激光雷达导向检测器构建了任务感知的超四维雷达点云。HyperDet通过聚合连续多帧中多个环视四维雷达的返回点来提升覆盖范围和点云密度，随后在重叠区域外采用轻量级自一致性校验进行几何感知的跨传感器一致性验证，以抑制不一致的返回点。此外，框架集成了一个前景聚焦的扩散模块，该模块在训练时结合雷达-激光雷达混合监督，以在提升雷达属性（如多普勒频移、雷达散射截面）的同时稠化目标结构；该模型被蒸馏为一个一致性模型，以实现单步推理。在MAN TruckScenes数据集上的实验表明，HyperDet在使用VoxelNeXt和CenterPoint检测器时，相比原始雷达输入均取得稳定提升，部分缩小了雷达与激光雷达之间的性能差距。这些结果表明，输入层面的精细化处理能使雷达更好地利用激光雷达导向的检测器，而无需修改其网络结构。