DQ3D: Depth-guided Query for Transformer-Based 3D Object Detection in Traffic Scenarios
This work addresses false positives in 3D object detection for autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problem of false positive detections in 3D object detection from multi-view images in traffic scenarios by proposing a depth-guided query generator and a hybrid attention mechanism, resulting in a 6.3% improvement in mAP and 4.3% in NDS on the nuScenes dataset.
3D object detection from multi-view images in traffic scenarios has garnered significant attention in recent years. Many existing approaches rely on object queries that are generated from 3D reference points to localize objects. However, a limitation of these methods is that some reference points are often far from the target object, which can lead to false positive detections. In this paper, we propose a depth-guided query generator for 3D object detection (DQ3D) that leverages depth information and 2D detections to ensure that reference points are sampled from the surface or interior of the object. Furthermore, to address partially occluded objects in current frame, we introduce a hybrid attention mechanism that fuses historical detection results with depth-guided queries, thereby forming hybrid queries. Evaluation on the nuScenes dataset demonstrates that our method outperforms the baseline by 6.3\% in terms of mean Average Precision (mAP) and 4.3\% in the NuScenes Detection Score (NDS).