# Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder

arXiv: https://arxiv.org/abs/2304.03420 · HTML: https://mlsanity.com/paper/2304.03420/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors](https://mlsanity.com/paper/1808.10322/) (2018)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Anomalous sound detection based on interpolation deep neural network](https://mlsanity.com/paper/2005.09234/) (2020)
- [Self-Supervised Out-of-Distribution Detection in Brain CT Scans](https://mlsanity.com/paper/2011.05428/) (2020)
- [A framework for anomaly detection using language modeling, and its applications to finance](https://mlsanity.com/paper/1908.09156/) (2019)
- [Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis](https://mlsanity.com/paper/1907.09254/) (2019)
- [A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics](https://mlsanity.com/paper/1811.12119/) (2018)

## Built on by (most cited first, 18 of 18)

- [Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection](https://mlsanity.com/paper/2505.07375/) (2025)
- [Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework](https://mlsanity.com/paper/2409.13162/) (2024)
- [Towards Zero-shot 3D Anomaly Localization](https://mlsanity.com/paper/2412.04304/) (2024)
- [Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation](https://mlsanity.com/paper/2505.16985/) (2025)
- [STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy](https://mlsanity.com/paper/2309.11006/) (2023)
- [Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset](https://mlsanity.com/paper/2311.04095/) (2023)
- [C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor](https://mlsanity.com/paper/2508.01311/) (2025)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection](https://mlsanity.com/paper/2508.20492/) (2025)
- [Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder](https://mlsanity.com/paper/2306.04466/) (2023)
- [OpenPatch: a 3D patchwork for Out-Of-Distribution detection](https://mlsanity.com/paper/2310.03388/) (2023)
- [Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor](https://mlsanity.com/paper/2410.09861/) (2024)
- [Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data](https://mlsanity.com/paper/2502.03876/) (2025)
- [SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds](https://mlsanity.com/paper/2506.13224/) (2025)
- [SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation](https://mlsanity.com/paper/2506.21892/) (2025)
- [Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation](https://mlsanity.com/paper/2506.22375/) (2025)
- [VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment](https://mlsanity.com/paper/2606.04369/) (2026)

## Abstract (arXiv)

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on the ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method. Our code is available at https://github.com/llien30/point_cloud_anomaly_detection.