# TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder Source: [TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder]() Collection index: [llms.txt]() Data updated: 2025-12-17T05:03:45.767Z Authors: Qinghao Meng, Chenming Wu, Liangjun Zhang, Jianbing Shen Published: 2025-12-12T00:08:03.000Z ## Abstract 3D object detection is essential in autonomous driving, providing vital information about moving objects and obstacles. Detecting objects in distant regions with only a few LiDAR points is still a challenge, and numerous strategies have been developed to address point cloud sparsity through densification.This paper presents a joint completion and detection framework that improves the detection feature in sparse areas while maintaining costs unchanged. Specifically, we propose TransBridge, a novel transformer-based up-sampling block that fuses the features from the detection and completion networks.The detection network can benefit from acquiring implicit completion features derived from the completion network. Additionally, we design the Dynamic-Static Reconstruction (DSRecon) module to produce dense LiDAR data for the completion network, meeting the requirement for dense point cloud ground truth.Furthermore, we employ the transformer mechanism to establish connections between channels and spatial relations, resulting in a high-resolution feature map used for completion purposes.Extensive experiments on the nuScenes and Waymo datasets demonstrate the effectiveness of the proposed framework.The results show that our framework consistently improves end-to-end 3D object detection, with the mean average precision (mAP) ranging from 0.7 to 1.5 across multiple methods, indicating its generalization ability. For the two-stage detection framework, it also boosts the mAP up to 5.78 points. ## 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. - [3D Object Detection on Waymo Open Dataset (WOD) (val) (L1/L2 AP and APH)](): 3D Object Detection; Waymo Open Dataset (WOD) (val); L1 3D AP: 80.94. ## Original sources and implementation [Original paper]() [PDF]()