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• UniDA3D formulates nighttime, rainy, and foggy scenes as a unified multi-target domain adaptation problem.method 90%
• UniDA3D introduces a query guided domain discrepancy mitigation (QDDM) module to align object features between source and target domains.method 90%
• The QDDM module performs alignment at both batch and global levels via query-centric adversarial and contrastive learning.technical 85%
• UniDA3D uses a domain-adaptive teacher-student training pipeline with an exponential-moving-average teacher and dynamically updated pseudo labels.method 90%
• UniDA3D performs a single unified training process across multiple adverse weather domains, unlike prior approaches that require separate training for each condition.method 90%
• UniDA3D consistently outperforms state-of-the-art camera-only multi-view 3D detectors under extreme conditions like night, rain, and haze.result 85%
• Existing multi-view 3D object detection methods suffer significant performance degradation under complex environmental conditions like nighttime, fog, and rain.limitation 80%
• The method maintains real-time inference efficiency while improving robustness.result 80%

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