GRADE: 視界不良下での単一フレーム生成レーダー深度推定
GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation
煙や霧、暗闇でも使えるmmWaveレーダーの単一フレームから、生成モデルの事前知識を活用して高精度なメートル深度を推定する手法を提案。
著者: Bin Zhao, Patrick Chiou, Nakul Garg
分類: cs.CV, cs.RO
原文アブストラクト
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.