R$^3$D: 基于区域引导的残差雷达扩散
R$^3$D: Regional-guided Residual Radar Diffusion
AI总结：
R3D通过区域引导残差雷达扩散框架，整合残差建模与sigma自适应引导，提升雷达点云质量，优于现有方法。
摘要：
毫米波雷达在恶劣条件下为自动驾驶系统提供了可靠的环境感知能力，但其点云稀疏、噪声大且角分辨率低。现有基于扩散的雷达增强方法要么因建模完整的LiDAR分布而带来高学习复杂度，要么因均匀区域处理而无法优先处理关键结构。为解决这些问题，我们提出了R3D，一种区域引导残差雷达扩散框架，该框架整合了残差扩散建模——专注于集中LiDAR-雷达残差编码以互补高频细节以降低学习难度——以及sigma自适应区域引导——利用雷达特定的信号特性生成注意力图，并在低噪声阶段应用轻量级引导以避免梯度不平衡的同时细化关键区域。在ColoRadar数据集上的大量实验表明，R3D优于现有最先进方法，提供了一种实用的雷达感知增强解决方案。我们的匿名代码和预训练模型已在此发布：https://anonymous.4open.science/r/r3d-F836
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
