Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes

2025-08-24

AI Summary

This work addresses open-set segmentation in large-scale outdoor LiDAR point clouds—specifically, detecting anomalous objects outside the trained semantic classes. Methodologically, it introduces the Mamba architecture to point cloud open-set segmentation for the first time, integrating insights from object defect detection to enhance feature discriminability via long-range dependency modeling. Coupled with a reconstruction-based learning paradigm and efficient point cloud processing techniques, the approach enables robust modeling of complex outdoor scenes. Evaluated on a large real-world point cloud dataset, the method achieves performance comparable to state-of-the-art voxel-based convolutional approaches, while significantly improving anomaly detection recall and cross-class generalization over existing open-set segmentation models. These advances provide a novel technical foundation for applications including autonomous driving, robotic navigation, and remote sensing monitoring.

Technology Category

• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Learning with Manifolds

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

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.

Problem

• Detecting outlier objects in large-scale outdoor LiDAR point clouds
• Addressing open-set segmentation for robotics and autonomous vehicles
• Improving anomaly detection performance in large-scale 3D scenes

Innovation

• Mamba architecture for long-range dependencies
• Reconstruction based open-set segmentation
• Outlier detection in large pointcloud scenes