用Mamba架构提升大规模点云异常检测，识别训练外的异常物体。
Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes
- 基于重构方法与Mamba架构，捕捉长距离依赖关系
- 在大型点云场景中显著提升开放集分割性能
- 适合机器人、自动驾驶等需要实时异常感知的场景
户外LiDAR扫描可获取大范围高精度距离数据，生成大规模点云，广泛应用于机器人、自动驾驶和土地监测。在实际应用中，训练数据之外的异常物体不可避免地会出现。本文提出一种新型开放集分割方法，借鉴物体缺陷检测的研究成果，并融合Mamba架构在长程依赖建模和大规模数据扩展性上的优势。通过构建基于重构的框架，实现了对室外场景中开放集异常的高效检测。实验表明，该方法不仅提升了自身性能，还能有效增强现有方法的表现。此外，我们提出的Mamba基架构在挑战性的大规模点云数据上，达到了与现有体素卷积方法相当的水平。
原文摘要 · Abstract (English)
LiDAR scanning in outdoor scenes acquires accurate distance measurements over wide areas, producing large-scale point clouds. Application examples for this data include robotics, automotive vehicles, and land surveillance. During such applications, outlier objects from outside the training data will inevitably appear. Our research contributes a novel approach to open-set segmentation, leveraging the learnings of object defect-detection research. We also draw on the Mamba architecture's strong performance in utilising long-range dependencies and scalability to large data. Combining both, we create a reconstruction based approach for the task of outdoor scene open-set segmentation. We show that our approach improves performance not only when applied to our our own open-set segmentation method, but also when applied to existing methods. Furthermore we contribute a Mamba based architecture which is competitive with existing voxel-convolution based methods on challenging, large-scale pointclouds.
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