HyperDet: 基于超4D雷达点云的3D目标检测
HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
AI总结：
提出一种与检测器无关的框架HyperDet，通过构建任务感知的超4D雷达点云，利用时空累积、跨传感器验证和多普勒引导的运动补偿以及前景生成增强，显著提升仅用雷达的3D目标检测性能。
摘要：
仅使用4D雷达进行3D目标检测能达到什么程度？尽管现代4D雷达为自主感知提供了鲁棒天气和速度感知能力，但其点云仍然稀疏、嘈杂且不稳定，限制了仅用雷达的3D检测。我们提出HyperDet，一种与检测器无关的框架，在检测前构建任务感知的超4D雷达点云。HyperDet首先通过时空累积、跨传感器验证和多普勒引导的运动补偿来细化短窗口环视雷达观测，提高返回可靠性和时间一致性。然后，它利用仅在训练时可用的激光雷达引导的伪雷达监督进行前景生成增强，在保留测量雷达背景和雷达原生属性的同时丰富目标几何。在检测器训练期间，雷达感知的目标级增强进一步在几何重定位下保持多普勒一致性。在推理时，HyperDet仅需雷达输入，可直接与标准3D检测器配合使用。在两个公开的环视4D雷达数据集上的实验表明，与原始雷达输入相比，在标准3D检测器上均取得一致改进，验证了输入级雷达增强作为仅用雷达3D检测的有效方法。
英文摘要：
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 input enhancement pipeline that constructs task- aware hyper 4D radar point clouds by combining measured observations with completed foreground geometry. HyperDet first refines short-window surround-view radar observations through spatio-temporal accumulation and cross-sensor val- idation, while Doppler-guided motion compensation reduces dynamic object trails when motion can be estimated reliably. It then performs foreground generative enhancement using LiDAR-guided pseudo-radar supervision available only during training, enriching object geometry while preserving measured radar background and radar-native attributes. During detec- tor training, radar-aware object-level augmentation maintains Doppler consistency under geometric relocation. At inference, 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 im- provements over matched temporal accumulation across stan- dard 3D detectors, validating input-level radar enhancement as an effective approach to radar-only 3D detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
