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arXiv Computer Vision

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

GRADE is a method for estimating high‑fidelity metric depth from a single radar frame, even when visual sensors fail due to smoke, fog, or darkness. It first converts raw 4D radar spectra into coarse depth, then uses a latent diffusion model conditioned on this estimate to recover fine structural detail. A pixel‑space adapter incorporates any available camera cues and is trained across clear, smoke‑degraded, and occluded inputs, allowing the output to rely more on radar as visibility worsens. On a dataset of ~95K frames from 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.

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arXiv Computer Vision
Sep 18

4D Radar Perception Algorithms for Autonomous Driving: A Review

The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.

By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
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arXiv Computer Vision
2d ago

DyRAD: Radar Novel View Synthesis for Dynamic Driving Scenes

DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.

By Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany
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arXiv Computer Vision
Sep 22

MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques

arXiv:2411.00527v5 Announce Type: replace-cross Abstract: Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as aut...

By Vanessa Wirth, Johanna Br\"aunig, Nikolai Hofmann, Martin Vossiek, Tim Weyrich, Marc Stamminger
agentsroboticsmultimodal
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arXiv Computer Vision
Sep 7

Weather-Conditioned Depth Anything

Weather-Conditioned Depth Anything (DA‑W) is a new framework that enhances monocular depth estimation models, like the Depth Anything series, to perform robustly under adverse weather conditions such as fog, rain, snow, and low‑light. It achieves this by disentangling style from content: a Style Filter extracts weather‑specific embeddings from a curated mix of real and synthetic degradation data, which are then injected into the backbone via a lightweight, zero‑initialized adapter. The adapter is trained with pseudo‑label distillation and alignment, enabling a single unified model to adapt to diverse weather scenarios while preserving its generalization on clean data, and it achieves state‑of‑the‑art performance with an average 3.7% improvement in AbsRel on weather benchmarks.

By Zhaoming Xu, Chan-Wei Hu, Kuan-Ru Huang, Zihao Zhu, Renjie Li, Yang Zhou, Zhengzhong Tu
ragcomputer-visionefficiencybenchmarkssafety
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