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：四维毫米波雷达提供天气鲁棒、速度感知的测量数据，且比激光雷达更具成本效益。然而，纯雷达三维检测性能仍落后于基于激光雷达的系统，因为雷达点云具有稀疏性、不规则性，且常受多径噪声干扰，导致几何特征弱且不稳定。本文提出HyperDet，一种与检测器无关的纯雷达三维检测框架，可为标准的激光雷达导向检测器构建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与点云密度，随后采用几何感知的跨传感器一致性验证机制，在重叠区域外进行轻量级自一致性校验以抑制不一致的返回点。该框架进一步集成前景聚焦的扩散模块，并辅以训练时混合雷达-激光雷达监督，在增强雷达属性（如多普勒频移、雷达截面积）的同时稠化目标结构；该模型通过蒸馏转化为可实现单步推理的一致性模型。在MAN TruckScenes数据集上的实验表明，HyperDet结合VoxelNeXt与CenterPoint检测器时，相较于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的性能差距。这些结果证明，通过输入级精细化处理，雷达能够在无需修改网络架构的情况下更好地适配激光雷达导向的检测器。