SpaceFormer: Spatial Position Contextual Semantics Embedding for Multi-View 3D Object Detection

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.