发表机构
Rice University（莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GRADE利用单帧雷达几何和生成先验，在视觉退化下实现高保真度量深度估计，在真实烟雾中MAE达0.313米，优于基线。
AI 中文摘要
在烟雾、雾气和黑暗中，由于光学传感器无法穿透空气中的微粒，稠密的三维深度感知会失效。毫米波雷达在这些条件下仍然可用，并能准确测量距离，但其小孔径限制了角分辨率。我们提出了GRADE，它将预训练的生成先验锚定在单帧雷达几何中，以估计高保真度的度量深度。GRADE首先将原始的4D雷达频谱映射为粗略的度量深度。随后，一个潜在扩散主干在每一步去噪过程中都基于该估计进行条件化，从而恢复结构细节。一个像素空间适配器在可用时利用残余的相机线索，并在清晰、烟雾退化和遮挡输入上进行训练，使得随着可见度退化，完整输出趋近于雷达条件化路径。我们在12栋建筑的真实烟雾场景中，使用约95K帧进行了训练和评估，GRADE在清晰场景中实现了0.303米的平均绝对误差（MAE），在烟雾下为0.313米，优于现有基线。代码和数据集可在该https URL获取。
英文摘要
Dense 3D depth perception fails under smoke, fog, and darkness because optical sensors cannot penetrate airborne particulates. mmWave radar remains usable and measures range accurately under these conditions, but its small aperture limits angular resolution. We present GRADE, which grounds a pretrained generative prior in single-frame radar geometry to estimate high-fidelity metric depth. GRADE first maps raw 4D radar spectra to coarse metric depth. A latent diffusion backbone then recovers structural detail while conditioning every denoising step on this estimate. A pixel-space adapter uses residual camera cues when available and is trained across clear, smoke-degraded, and occluded inputs so the full output approaches the radar-conditioned path as visibility degrades. Trained and evaluated on ~95K frames across 12 buildings with real smoke, GRADE achieves an MAE of 0.303 m in clear scenes and 0.313 m under smoke, outperforming existing baselines. Code and datasets are available at https://phi-lab-rice.github.io/GRADE.
CommentsTo appear in ACM MobiCom 2026
