# DQ3D: Depth-guided Query for Transformer-Based 3D Object Detection in Traffic Scenarios Source: [DQ3D: Depth-guided Query for Transformer-Based 3D Object Detection in Traffic Scenarios]() Collection index: [llms.txt]() Data updated: 2025-10-29T05:04:11.035Z Authors: Ziyu Wang, Wenhao Li, Ji Wu Published: 2025-10-27T09:20:59.000Z ## Abstract 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()