New framework enhances depth estimation models for adverse weather conditions

Researchers have developed Weather-Conditioned Depth Anything (DA-W), a new framework designed to improve monocular depth estimation models under adverse weather conditions. By disentangling weather-related style from content, DA-W uses a Style Filter to generate weather embeddings that are injected into the Depth Anything backbone via a parameter-efficient adapter. This approach allows a single model to adapt to various conditions like fog, rain, and snow without losing its performance on clear-weather benchmarks. Experiments show DA-W achieves state-of-the-art robust depth estimation, improving AbsRel scores by an average of 3.7% on weather-specific benchmarks. AI

IMPACT Enhances the robustness of depth estimation models, enabling more reliable AI applications in challenging environmental conditions.

RANK_REASON The cluster contains a research paper detailing a new framework for improving computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• AbsRel
• arXiv
• DagsHub
• Da-Wen Sun
• Depth Anything
• Hugging Face
• Weather-Conditioned Depth Anything

• paper
• model release

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