用激光雷达监督，让毫米波雷达去噪更准，适合自动驾驶
CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving
- 用激光雷达标注噪声，指导雷达数据去噪
- 在双雷达数据集上检测精度显著提升
- 训练用激光雷达，推理全靠雷达，部署无负担
基于4D毫米波雷达的目标检测因其在恶劣天气下的鲁棒性以及在多种驾驶场景中提供丰富空间信息的能力而受到广泛关注。然而，4D雷达点云稀疏且噪声大，给有效感知带来巨大挑战。为解决此问题，我们提出CORENet，一种新颖的跨模态去噪框架，利用激光雷达监督来识别噪声模式，并从原始4D雷达数据中提取判别性特征。该方案设计为即插即用架构，可无缝集成到基于体素的检测框架中，无需修改现有流程。值得注意的是，所提方法仅在训练阶段使用激光雷达数据进行跨模态监督，推理阶段保持纯雷达运行。在噪声水平较高的双雷达数据集上的大量实验表明，该框架能显著提升检测鲁棒性。全面实验验证了CORENet相较于现有主流方法的优越性能。
原文摘要 · Abstract (English)
4D radar-based object detection has garnered great attention for its robustness in adverse weather conditions and capacity to deliver rich spatial information across diverse driving scenarios. Nevertheless, the sparse and noisy nature of 4D radar point clouds poses substantial challenges for effective perception. To address the limitation, we present CORENet, a novel cross-modal denoising framework that leverages LiDAR supervision to identify noise patterns and extract discriminative features from raw 4D radar data. Designed as a plug-and-play architecture, our solution enables seamless integration into voxel-based detection frameworks without modifying existing pipelines. Notably, the proposed method only utilizes LiDAR data for cross-modal supervision during training while maintaining full radar-only operation during inference. Extensive evaluation on the challenging Dual-Radar dataset, which is characterized by elevated noise level, demonstrates the effectiveness of our framework in enhancing detection robustness. Comprehensive experiments validate that CORENet achieves superior performance compared to existing mainstream approaches.
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