发表机构
Korea Advanced Institute of Science and Technology（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出立体4D雷达的3D目标检测框架，利用左右雷达几何视差估计物体绝对速度并融合互补特征，在内部数据集上较单目4D雷达基线实现AP 3D提升8.82点、AP BEV提升9.0点。
AI 中文摘要
四维（4D）雷达是一种强大的传感模态，能够在各种天气条件下检测周围的三维（3D）物体，并提供基于多普勒的运动信息。然而，原始4D雷达信号包含来自路面、护栏和周围车辆的大量杂波，以及多路径引起的虚假反射和接收机固有的噪声底。因此，用于去除此类无效测量值的预处理算法常常会使雷达数据变得过于稀疏。此外，4D雷达提供的多普勒测量值仅描述物体速度的径向分量，限制了其恢复完整运动状态的能力。在本文中，我们提出了一种基于立体4D雷达的3D目标检测框架，该框架利用左右雷达之间的几何视差来估计物体的绝对速度，并通过融合它们的互补特征实现更鲁棒的感知。所提出框架的有效性在我们的内部立体4D雷达数据集上得到验证，与最先进的单目4D雷达基线相比，在AP 3D上取得了8.82个点的性能提升，在AP BEV上取得了9.0个点的性能提升。这些结果表明，绝对速度估计结合立体几何感知特征融合可显著提升3D目标检测性能。
英文摘要
Four-dimensional (4D) Radar is a powerful sensing modality capable of detecting surrounding three-dimensional (3D) objects under diverse weather conditions and providing Doppler-based motion information. However, raw 4D Radar signals contain significant clutter from road surfaces, guardrails, and surrounding vehicles, along with multipath-induced ghost reflections and the receiver's inherent noise floor. Consequently, preprocessing algorithms designed to remove such invalid measurements often make the Radar data excessively sparse. Moreover, the Doppler measurements provided by 4D Radar describe only the radial component of an object's velocity, limiting their ability to recover the full motion state. In this paper, we introduce a stereo 4D Radar-based 3D object detection framework that exploits the geometric disparity between left and right Radars to estimate the absolute velocity of objects and achieve more robust perception through the fusion of their complementary features. The effectiveness of the proposed framework is validated on our in-house stereo 4D Radar dataset, demonstrating performance gains of 8.82 points in AP 3D and 9.0 points in AP BEV over state-of-the-art mono 4D Radar baselines. These results demonstrate that absolute velocity estimation combined with stereo geometry-aware feature fusion leads to substantial improvements in 3D object detection.
