利用多普勒信息优化雷达点云时序聚合，提升远距离目标检测精度。
DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection
- 根据多普勒分量沿径向移动历史点，减少动态物体引起的散射
- 按多普勒与角度动态分配聚合时长，有效抑制切向散射
- 可适配任意检测器，在多个数据集上显著提升检测性能
基于雷达的目标检测在自动驾驶中至关重要，因其具备远距离探测能力。然而，尤其在远距离时雷达点云稀疏，影响检测准确性。现有方法通过自车运动补偿进行时序聚合以增加点密度，但会引入动态物体带来的散射，降低检测性能。本文提出DoppDrive，一种基于多普勒的时序聚合新方法，在提升点云密度的同时最小化散射。该方法依据点的动态多普勒分量沿径向调整前帧点位置，消除径向散射，并根据多普勒与角度为每个点分配独特聚合时长，以最小化切向散射。DoppDrive作为检测前的点云密度增强步骤，兼容任意检测器，实验表明其在多种检测器与数据集上均显著提升检测性能。
原文摘要 · Abstract (English)
Radar-based object detection is essential for autonomous driving due to radar's long detection range. However, the sparsity of radar point clouds, especially at long range, poses challenges for accurate detection. Existing methods increase point density through temporal aggregation with ego-motion compensation, but this approach introduces scatter from dynamic objects, degrading detection performance. We propose DoppDrive, a novel Doppler-Driven temporal aggregation method that enhances radar point cloud density while minimizing scatter. Points from previous frames are shifted radially according to their dynamic Doppler component to eliminate radial scatter, with each point assigned a unique aggregation duration based on its Doppler and angle to minimize tangential scatter. DoppDrive is a point cloud density enhancement step applied before detection, compatible with any detector, and we demonstrate that it significantly improves object detection performance across various detectors and datasets.
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