仅用仿真数据训练，实现高精度城市激光雷达3D目标检测。
Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detection
- 通过密度对齐与多样采样构建30k条合成数据集。
- 多模型融合提升检测性能，3D mAP达0.1258。
- 适合关注仿真到真实迁移的自动驾驶研究者。
我们提交了在ECCV 2026第六届DriveX研讨会中UCF UrbanTwin Sim2Real LiDAR挑战赛的LUMPI赛道解决方案。检测器仅在合成数据上训练，评估使用50个未见的真实激光雷达帧；另50帧合成数据用于点云真实感评估。方法从三个层面缓解模拟到现实的差距：首先，将合成扫描对齐至50,000点测试密度，利用UT-LUMPI几何、RangeLDM采样多样化、稀有类别复制粘贴及行人导向增强构建30,000条训练样本；其次，在相同合成数据约束下训练互补的DSVT检测器与专用于车辆/公交车的PointPillars模型；第三，通过类别感知路由、非对称一致性融合、受限残差-召回补充、类别覆盖审计和选择性框尺寸校准整合预测结果。真实感分支独立优化，采用径向密度匹配、弱仿射校准和校准集合混合。最终提交结果为综合得分0.4692，检测得分0.1797，真实感得分0.9035，3D [email protected]为0.1258。
原文摘要 · Abstract (English)
We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data and is evaluated on 50 held-out real LiDAR frames; a separate 50-frame synthetic submission is evaluated for point-cloud realism. Our method addresses the Sim2Real gap at three levels. First, we align synthetic scans to the 50k-point test density and build a 30k-record training pool using UT-LUMPI geometry, RangeLDM-based sampling diversification, rare-class copy-paste, and pedestrian-oriented augmentation. Second, complementary DSVT detectors and Car/Bus PointPillars specialists are trained under the same synthetic-only constraint. Third, predictions are integrated by class-aware routing, asymmetric agreement fusion, constrained residual-recall supplementation, class-coverage auditing, and selective box-size calibration. The realism branch is optimized independently with radial-density matching, weak affine calibration, and calibrated set mixing. The final submission obtains a Combined Score of 0.4692, a Detection Score of 0.1797, a Realism Score of 0.9035, and 3D [email protected] of 0.1258.
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