首个面向自动驾驶的3D异常分割数据集，融合激光雷达与摄像头数据。
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
- 构建包含激光雷达与相机的多模态3D数据集，支持密集语义标注
- 首次提供带时序信息的3D异常分割数据，覆盖不同距离范围
- 开源数据与评测代码，助力自动驾驶异常检测研究
为确保安全运行，自动驾驶车辆需识别并应对道路上的意外物体或异常情况。尽管2D异常检测与分割已有较多研究，但3D方向仍处于探索阶段。现有数据集缺乏自动驾驶系统中常见的高质量多模态数据。本文提出一个面向驾驶场景的新型3D异常分割数据集。据我们所知，这是首个公开可用的专注于道路异常分割的3D数据集，具备密集3D语义标注，融合激光雷达与摄像头数据，并包含时序信息，支持跨距离的异常检测，对自动驾驶的安全导航至关重要。我们评估了多个基线3D分割模型，揭示了驾驶环境中3D异常检测的挑战。本数据集及评测代码将公开发布，便于不同方法的测试与性能对比。
原文摘要 · Abstract (English)
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segmentation in 2D, research progress in 3D is underexplored. Existing datasets lack high-quality multimodal data that are typically found in AVs. This paper presents a novel dataset for anomaly segmentation in driving scenarios. To the best of our knowledge, it is the first publicly available dataset focused on road anomaly segmentation with dense 3D semantic labeling, incorporating both LiDAR and camera data, as well as sequential information to enable anomaly detection across various ranges. This capability is critical for the safe navigation of autonomous vehicles. We adapted and evaluated several baseline models for 3D segmentation, highlighting the challenges of 3D anomaly detection in driving environments. Our dataset and evaluation code will be openly available, facilitating the testing and performance comparison of different approaches.
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