融合路侧相机与激光雷达点云，实现高精度协同3D目标检测。
Camera and LiDAR BEV Fusion for Cooperative 3D Object Detection on TUMTraf V2X
- 在共享鸟瞰图空间中融合三路摄像头与融合点云数据
- 3D mAP达0.85，重叠帧微调后提升至0.89，真值替换后达0.99
- 适用于智能交通系统中的协同感知，适合关注数据泄露影响的研究者
本文介绍为DriveX 2026挑战赛中TUMTraf V2X协同3D目标检测任务开发的相机与激光雷达融合检测器。该检测器将三路路边摄像头与融合的基础设施加车辆点云在共享鸟瞰图空间中融合，并通过类似CenterPoint的头部结合广义IoU回归损失和IoU质量重排序头预测边界框。在提供的训练集和验证集上训练后，模型在公开Codabench测试集上达到0.85的3D mAP。研究发现，测试集中的50帧中有44帧出现在已发布的训练集（40帧）和验证集（4帧）中且带有标注。为此，我们进行了两项额外分析：（1）对44个重叠帧进行过采样微调，mAP提升至0.89；（2）对这些帧的预测结果用真实标签替换，得到0.99 mAP（已上传至Codabench用于测试，未发布于排行榜）。三种配置及其类别级结果均已报告。
原文摘要 · Abstract (English)
We describe a Camera and LiDAR fusion detector developed for the TUMTraf V2X cooperative 3D object detection track of the DriveX 2026 challenge. The detector fuses three roadside cameras with a fused infrastructure-plus-vehicle point cloud in a shared bird's-eye-view space and predicts boxes through a CenterPoint-style head with a generalized IoU regression loss and an IoU quality re-ranking head. Trained on the provided train and validation splits, the model reaches a 3D mAP of 0.85 on the public Codabench test split. While iterating on the system, we observed that 44 of the 50 test frames are also present in the released train (40) and validation (4) splits with their labels. We therefore conducted two additional studies to quantify how this overlap affects the final score: (1) a finetuning run that oversamples the 44 overlapping frames, reaching 0.89 mAP, and (2) a post-processing run that replaces predictions on those frames with the released ground truth, reaching 0.99 mAP (uploaded to our Codabench account for testing but not published on the leaderboard). All three configurations and their per-class results are reported.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。