Multi-view 3D object detection plays a vital role in autonomous driving systems due to its ability to perceive complex scenes accurately. However, real-world driving data often exhibits a long-tailed distribution, causing significant drops in detection accuracy for rare categories in existing methods. To mitigate this issue, we propose CLIPDet3D, a novel vision-language collaborative framework for multi-view 3D object detection. First, to tackle the difficulty of capturing the semantic information of rare categories, a Vision-Language Collaborative Learning strategy is proposed to incorporate class-level semantic priors from CLIP. Second, a Depth Feature Contrastive Distillation module is designed to overcome the large depth estimation error for rare categories by aligning depth features between a teacher and a student network. Furthermore, to alleviate the difficulty in focusing on regions of rare categories, a Dual-Stream Prompt Attention mechanism is devised to inject learnable prompts and compute attention along both horizontal and vertical BEV directions. Evaluations on the nuScenes dataset demonstrate that CLIPDet3D achieves state-of-the-art accuracy while maintaining efficient inference.

3D object detection aims to accurately localize and recognize objects in 3D space. It serves as a fundamental task for reliable perception in intelligent transportation systems, enabling the monitoring of diverse traffic participants such as vehicles, pedestrians, cyclists, and public transport. Recently, transformer-based methods have gained significant attention in multi-view 3D object detection due to their strong global reasoning capabilities. However, their limited capacity to model spatial positional information hinders accurate object localization, especially in complex and large-scale scenes. To address this limitation, SpaceFormer is proposed as a novel transformer-based multi-view 3D object detector. Specifically, a Contextual Visual Prompts Learning strategy is proposed to enhance the perception of small and sparse traffic participants by incorporating contextual priors. To further suppress background interference, a Semantics-guided Depth Estimation method is proposed to refine depth representations using high-level semantic information. Furthermore, a Spatial Position Embedding mechanism is proposed to improve the spatial localization capability of the transformer by integrating geometric position and polar spatial embedding. Extensive experiments on the nuScenes benchmark demonstrate that SpaceFormer achieves state-of-the-art performance with 55.5% mAP and 62.9% NDS. These improvements indicate not only methodological advances but also practical benefits for intelligent transportation systems, enhancing safety, reliability, and efficiency in real-world deployments.