Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes
This addresses the challenge of handling unknown objects in outdoor scenes for domains such as autonomous vehicles, though it appears incremental by building on prior defect-detection and Mamba architecture research.
The paper tackles the problem of detecting outlier objects in large-scale outdoor LiDAR point clouds for applications like robotics and surveillance, introducing a novel open-set segmentation method that improves performance on both their own and existing methods.
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.