用增强雷达点云提升纯雷达3D检测精度
HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
- 构建任务感知的超4D雷达点云，融合时空积累与多传感器验证
- 训练时利用激光雷达监督生成更完整的物体几何结构
- 推理时仅需雷达输入，可兼容主流3D检测器
纯4D雷达在自动驾驶感知中具有抗恶劣天气和速度感知优势，但其点云稀疏、噪声大且不稳定，限制了纯雷达3D检测性能。本文提出HyperDet，一种检测无关的输入级增强框架。它通过短窗口内时空累积、跨传感器验证及多普勒引导的运动补偿，提升回波可靠性与时间一致性；训练阶段借助仅在训练时可用的激光雷达引导伪雷达监督，实现前景生成增强，在保留真实雷达背景和原生属性的同时丰富物体几何；推理时无需其他传感器，直接输出增强雷达点云，可无缝接入标准3D检测器。在两个公开的环视4D雷达数据集上的实验表明，该方法在多种标准检测器上均显著优于原始雷达输入，验证了输入级雷达增强对纯雷达3D检测的有效性。
原文摘要 · Abstract (English)
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.
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