Ghost-FWL：面向鬼影检测与去除的大规模全波形激光雷达数据集
Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal
对做移动激光雷达感知的人，这篇提供了用全波形时序强度剖面（而非仅峰值距离）判别多路径鬼影的思路，并给出可复用的标注数据集与基线，可直接接入 SLAM 前端与 3D 检测的预处理环节。
原文依据 exploiting full-waveform LiDAR (FWL), which captures complete temporal intensity profiles rather than just peak distances
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论激光雷达已成为自动驾驶、机器人与智慧城市应用中不可或缺的传感模态。然而，由玻璃与反射表面的多路径激光回波造成的虚假反射（即鬼影点，简称鬼影）会严重降低三维建图与定位精度。已有的鬼影去除方法依赖稠密点云中的几何一致性，在移动激光雷达的稀疏、动态数据上会失效。我们通过利用全波形激光雷达（FWL）来解决这一问题：FWL 捕获的是完整的时序强度剖面，而不仅是峰值距离，从而为在移动场景中区分鬼影与真实反射提供了关键线索。由于这是一项新任务，我们提出了 Ghost-FWL，这是首个也是规模最大的、面向鬼影检测与去除的移动 FWL 标注数据集。Ghost-FWL 包含覆盖 10 个多样场景的 24K 帧，带有 75 亿条峰值级标注，规模是现有标注 FWL 数据集的 100 倍。实验表明，我们的基线在鬼影去除精度上优于现有方法，且我们的鬼影去除进一步提升了基于激光雷达的 SLAM（轨迹误差降低 66%）与 3D 目标检测（误报减少 50 倍）等下游任务。
英文原文摘要 1199 字符
LiDAR has become an essential sensing modality in autonomous driving, robotics, and smart-city applications. However, ghost points (or ghost), which are false reflections caused by multi-path laser returns from glass and reflective surfaces, severely degrade 3D mapping and localization accuracy. Prior ghost removal rely on geometric consistency in dense point clouds, failing on mobile LiDAR's sparse, dynamic data. We address this by exploiting full-waveform LiDAR (FWL), which captures complete temporal intensity profiles rather than just peak distances, providing crucial cues for distinguishing ghosts from genuine reflections in mobile scenarios. As this is a new task, we present Ghost-FWL, the first and largest annotated mobile FWL dataset for ghost detection and removal. Ghost-FWL comprises 24K frames across 10 diverse scenes with 7.5 billion peak-level annotations, which is 100x larger than existing annotated FWL datasets. Experiments show that our baseline outperforms existing methods in ghost removal accuracy, and our ghost removal further enhance downstream tasks such as LiDAR-based SLAM (66% trajectory error reduction) and 3D object detection (50x false positive reduction).