在干草堆中寻找异常值：大型点云场景的异常检测
Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes
AI总结：
针对户外大型点云场景中训练数据外异常对象的问题，提出一种结合对象缺陷检测研究成果与Mamba架构优势的基于重建的开集分割方法，可提升自身及现有方法性能，且基于Mamba的架构能与现有体素卷积方法竞争。
摘要：
户外场景中的LiDAR扫描可在广域获取精确距离测量值，生成大规模点云。此类数据的应用实例包括机器人、汽车和土地监测。在此类应用中，训练数据之外的异常对象将不可避免地出现。本研究提出一种新颖的开集分割方法，借鉴了对象缺陷检测研究的成果，同时利用Mamba架构在利用长程依赖和大规模数据可扩展性方面的优异性能。结合两者，我们创建了一种基于重建的户外场景开集分割方法。结果表明，该方法不仅在应用于我们自己的开集分割方法时提升了性能，还能改善现有方法的性能；此外，我们提出的基于Mamba的架构在具有挑战性的大规模点云上可与现有基于体素卷积的方法竞争。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
