GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation — Paper

Abstract

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.

Authors 3

Bin Zhao, Patrick Chiou, Nakul Garg

Specification

Official page: arxiv.org/abs/2609.10756
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.10756
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.CV, cs.RO
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
DOI: 10.1145/3795866.3844478
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.10756
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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10

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T110

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2 h ago

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Authors: Bin Zhao, Patrick Chiou, Nakul Garg

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Value	Valid from → to	Status	Source	Confidence	Extractor
cs.CV	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperGRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation
New paper: GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation
arxiv
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	2 h ago	1

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