用激光雷达知识提升雷达感知，让低成本雷达更准识物
LEROjD: Lidar Extended Radar-Only Object Detection
- 分阶段稀疏激光点云，逐步迁移知识到雷达模型
- 雷达检测精度最高提升4.2个百分点，知识蒸馏达3.9个百分点
- 方法通用性强，可适配多种3D检测网络
精准的3D目标检测对自动驾驶至关重要。尽管激光雷达在此任务中表现优异，但其成本高且在恶劣天气下性能受限。3+1D成像雷达传感器具有成本低、抗干扰强的优势，但受分辨率低和测量噪声大的制约。现有3+1D成像雷达数据集包含雷达与激光雷达数据，支持跨模态模型优化。虽然推理时不应使用激光雷达，但在训练雷达仅模型时可借助其辅助。本文探索两种从激光雷达域向雷达域及雷达仅检测器迁移知识的策略：1. 通过逐级稀疏激光点云的多阶段训练；2. 跨模态知识蒸馏。多阶段训练中测试了三种稀疏方法。实验表明，多阶段训练使平均精度（mAP）最高提升4.2个百分点，知识蒸馏则达3.9个百分点，且初始化学生模型权重来自教师模型。主要优势在于该方法可应用于其他3D目标检测网络而无需修改其结构，我们在两个不同检测器上进行了验证。代码已开源。
原文摘要 · Abstract (English)
Accurate 3D object detection is vital for automated driving. While lidar sensors are well suited for this task, they are expensive and have limitations in adverse weather conditions. 3+1D imaging radar sensors offer a cost-effective, robust alternative but face challenges due to their low resolution and high measurement noise. Existing 3+1D imaging radar datasets include radar and lidar data, enabling cross-modal model improvements. Although lidar should not be used during inference, it can aid the training of radar-only object detectors. We explore two strategies to transfer knowledge from the lidar to the radar domain and radar-only object detectors: 1. multi-stage training with sequential lidar point cloud thin-out, and 2. cross-modal knowledge distillation. In the multi-stage process, three thin-out methods are examined. Our results show significant performance gains of up to 4.2 percentage points in mean Average Precision with multi-stage training and up to 3.9 percentage points with knowledge distillation by initializing the student with the teacher's weights. The main benefit of these approaches is their applicability to other 3D object detection networks without altering their architecture, as we show by analyzing it on two different object detectors. Our code is available at https://github.com/rst-tu-dortmund/lerojd
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