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

arXiv: https://arxiv.org/abs/2508.17634 · HTML: https://mlsanity.com/paper/2508.17634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 18 of 18)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [PointMamba: A Simple State Space Model for Point Cloud Analysis](https://mlsanity.com/paper/2402.10739/) (2024)
- [MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.06564/) (2024)
- [Pixel-wise Anomaly Detection in Complex Driving Scenes](https://mlsanity.com/paper/2103.05445/) (2021)
- [Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy](https://mlsanity.com/paper/2403.06467/) (2024)
- [Open-world Semantic Segmentation for LIDAR Point Clouds](https://mlsanity.com/paper/2207.01452/) (2022)
- [3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering](https://mlsanity.com/paper/2404.05522/) (2024)
- [MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model](https://mlsanity.com/paper/2404.12794/) (2024)
- [ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation](https://mlsanity.com/paper/2404.10699/) (2024)
- [SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds](https://mlsanity.com/paper/2505.10601/) (2025)
- [Conditional Reconstruction for Open-set Semantic Segmentation](https://mlsanity.com/paper/2203.01368/) (2022)
- [Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series](https://mlsanity.com/paper/2208.01998/) (2022)
- [ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement](https://mlsanity.com/paper/2407.17705/) (2024)
- [A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data](https://mlsanity.com/paper/2503.11097/) (2025)

## Built on by (most cited first, 1 of 1)

- [ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction](https://mlsanity.com/paper/2604.01081/) (2026)

## Abstract (arXiv)

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.