Problem
Research questions and friction points this paper is trying to address.
4D Radar
3D Object Detection
Doppler Measurement
Clutter
Absolute Velocity Estimation
Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation
🤖 AI Summary
本文提出一种基于立体4D雷达的3D物体检测框架，通过左右雷达几何差异估计物体绝对速度并融合互补特征，以解决雷达信号杂乱及运动状态恢复不全的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
stereo 4D radar
absolute velocity estimation
geometric disparity
feature fusion
3D object detection
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