Monocular 3D object detection offers significant potential for autonomous systems due to its inherent cost-effectiveness and scalability. While DETR-based architectures excel in 2D vision tasks, critical limitations persist in extending them effectively to monocular 3D detection, as evidenced in existing frameworks like MonoDETR and MonoDGP. These methods typically suffer from inefficient serial fusion of multimodal features and lack iterative refinement mechanisms, limiting their performance, especially for mid-to-long range targets. To overcome these shortcomings, we propose Iter3DDet, a novel depth-guided iterative refinement framework that integrates fine-grained feature fusion to significantly enhance detection performance. The core novelty of our approach lies in two key innovations: (1) A hybrid feature encoder combining MonoDGP’s region segmentation head with MonoDETR’s visual backbone, augmented by a multi-scale context attention module that dynamically aggregates structural and semantic cues across pyramid levels, eliminating heuristic fusion rules; (2) A depth-guided adaptive cross-modal decoder that iteratively fuses depth and context features through prioritized attention mechanisms, coupled with a novel iterative refinement training strategy that progressively refines 3D detection hypotheses, substantially improving accuracy across targets of varying difficulty levels. Extensive experiments on the KITTI, nuScenes, and Waymo benchmarks demonstrate Iter3DDet’s state-of-the-art performance, validating the effectiveness of our iterative refinement paradigm. The code will be open-sourced at https://github.com/PCwenyue.
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