MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection
This study addresses the poor cross-domain generalization in monocular temporal 3D detection caused by learnable query overfitting. We propose the first domain generalization method for this task, introducing a Domain-Robust Anchor Generator (DRAG) and Temporal Refinement Identity Merging (TRIM) strategy to effectively decouple spatial distribution dependencies and optimize temporal associations. Furthermore, we establish a comprehensive cross-dataset generalization benchmark. Experimental results demonstrate that our approach improves zero-shot cross-domain NDS from 12.1% to 18.6% while simultaneously enhancing in-domain accuracy. By comprehensively outperforming existing baselines, this work significantly strengthens model robustness and generalization capabilities across diverse domains.