Robo-DETR: Robustness-Aware Depth-Guided Transformer for Monocular 3-D Object Detection Under Adverse Visual Conditions

Monocular 3-D object detection is attractive for autonomous driving due to its low cost, but its performance degrades severely under adverse visual conditions that corrupt appearance cues and destabilize depth-related representations. We propose Robo-DETR, a robustness-aware depth-guided transformer framework that combines minimal visual-conditioned input preconditioning with detector-internal depth reliability correction. Specifically, a lightweight condition recognition and enhancement block (CREB) stabilizes low-level visual cues, while a condition-aware depth-guided block (CDB) refines depth logits via foreground-aware supervision and structured dynamic/region-level adjustment to suppress visual-induced depth noise. A dual-branch transformer decoder further promotes depth-appearance consistency through modality-specific cross-attention and fusion. Experiments on KITTI-C and the real-world TJ4DRadSet demonstrate consistent improvements over competitive monocular detectors under diverse degradations, and ablations validate the contribution of each component.