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Abstract Semantic segmentation models for autonomous driving suffer significant performance degradation under adverse weather and lighting conditions such as fog, rain, snow, and nighttime illumination. Existing approaches include restoration before segmentation and robustness-oriented adaptation or generalization; comparatively few use an image-derived degradation estimate as an explicit conditioning input to the segmentation network.
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