Tuning only 8.7M parameters of a frozen DINOv2 on daytime data is reported to beat prior PEFT, full fine-tuning, synthetic-data depth methods, and Depth Anything V2 on zero-shot adverse-weather benchmarks.
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ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation
Tuning only 8.7M parameters of a frozen DINOv2 on daytime data is reported to beat prior PEFT, full fine-tuning, synthetic-data depth methods, and Depth Anything V2 on zero-shot adverse-weather benchmarks.