用区域引导残差扩散，提升毫米波雷达点云的细节与结构精度
R$^3$D: Regional-guided Residual Radar Diffusion
- 聚焦雷达与激光雷达残差，编码高频细节降低学习难度
- 仅在低噪声阶段轻量引导关键区域，避免梯度失衡
- 在ColoRadar数据集上超越现有方法，适合自动驾驶感知场景
毫米波雷达在恶劣环境下具备鲁棒环境感知能力，但其点云稀疏、噪声大且角度分辨率低。现有基于扩散模型的雷达增强方法要么因建模全激光雷达分布而学习复杂度高，要么因均匀区域处理而忽略关键结构。为此，本文提出R3D：一种区域引导的残差雷达扩散框架，融合残差扩散建模（专注集中式激光雷达-雷达残差，编码互补高频细节以降低学习难度）与自适应方差区域引导（利用雷达信号特性生成注意力图，仅在低噪声阶段施加轻量引导，避免梯度失衡同时精炼关键区域）。在ColoRadar数据集上的大量实验表明，R3D优于当前最优方法，为雷达感知增强提供了实用解决方案。代码与预训练模型已公开。
原文摘要 · Abstract (English)
Millimeter-wave radar enables robust environment perception in autonomous systems under adverse conditions yet suffers from sparse, noisy point clouds with low angular resolution. Existing diffusion-based radar enhancement methods either incur high learning complexity by modeling full LiDAR distributions or fail to prioritize critical structures due to uniform regional processing. To address these issues, we propose R3D, a regional-guided residual radar diffusion framework that integrates residual diffusion modeling-focusing on the concentrated LiDAR-radar residual encoding complementary high-frequency details to reduce learning difficulty-and sigma-adaptive regional guidance-leveraging radar-specific signal properties to generate attention maps and applying lightweight guidance only in low-noise stages to avoid gradient imbalance while refining key regions. Extensive experiments on the ColoRadar dataset demonstrate that R3D outperforms state-of-the-art methods, providing a practical solution for radar perception enhancement. Our anonymous code and pretrained models are released here: https://anonymous.4open.science/r/r3d-F836
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