发表机构
Guangdong University of Technology; SenseTime（广东工业大学; 商汤科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SimFuse3D通过源引导目标模拟与置信度引导多阶段定位重加权解决跨平台3D目标检测的框-点不一致问题，在6次跨平台迁移任务中多数指标优于Pi3DET-Net，nuScenes到KITTI任务表现最优。
AI 中文摘要
传感器高度和视点的变化会改变物体级点分布，这使得跨平台激光雷达无监督域适应（UDA）任务变得困难。自训练方法利用带标注的源扫描数据和无标注的目标扫描数据，但保留的预测结果可能提供有用的目标位置，却包含稀疏的前景回波、背景杂波或与预测框不一致的点，我们将这种不匹配称为框-点不一致。本文提出SimFuse3D，该方法保留目标位置，并利用带标注源扫描数据的实测几何信息修复相关伪目标。其中，目标记忆模块（Object Memory）检索兼容的带标注源实例；目标模拟模块（Target Simulation）将其真值框置于目标位置，使点与目标视点几何对齐，并过滤对齐后的裁剪区域以近似目标观测结果；置信度引导多阶段定位重加权（CMLR）模块将每个目标伪目标的置信度分数映射为RPN定位和R-CNN框回归共享的有界权重。所有组件仅在适应阶段运行，检测器架构和推理图保持不变。在6次跨平台迁移任务中，SimFuse3D在所有报告的AP指标上均优于Pi3DET-Net，且在几乎所有指标上位列对比适应方法的首位；在nuScenes到KITTI的迁移任务中，其在两种被评估检测器上均位列对比适应方法的首位。
英文摘要
Changes in sensor height and viewpoint alter object-level point distributions, making cross-platform LiDAR unsupervised domain adaptation (UDA) difficult. Self-training uses labeled source scans and unlabeled target scans, yet a retained prediction may provide a useful target location while enclosing sparse foreground returns, background clutter, or points inconsistent with the predicted box. We refer to this mismatch as box-point inconsistency. We introduce SimFuse3D, which preserves the target placement and repairs the associated pseudo object using measured geometry from labeled source scans. Object Memory retrieves a similar labeled source instance. Target Simulation places the retrieved source geometry at the target location, aligns its points with the target viewing geometry, and filters the aligned crop to approximate the target observation. Confidence-Guided Multi-Stage Localization Reweighting (CMLR) maps each target pseudo-object confidence score to a bounded weight shared by RPN localization and R-CNN box regression. All components operate only during adaptation, leaving the detector architecture and inference graph unchanged. Across six cross-platform transfers, SimFuse3D consistently outperforms Pi3DET-Net and achieves the best performance among the compared adaptation methods on nearly all metrics. On nuScenes-to-KITTI, it ranks first among the compared adaptation methods with both evaluated detectors.
Comments9 pages, 5 figures. Submitted to ICRA
