mlsanityData to 14 Oct 2026

Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder

arXiv 2304.03420 ↗First posted 7 Apr 2023cs.CVcited by 18

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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.

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

16 of 23 references.

References · 16

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  3. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  4. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  5. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  6. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  7. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  8. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  9. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 239
  10. 2018PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptorscited by 141
  11. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  12. 2020Anomalous sound detection based on interpolation deep neural networkcited by 34
4 more
  1. 2020Self-Supervised Out-of-Distribution Detection in Brain CT Scanscited by 10
  2. 2019A framework for anomaly detection using language modeling, and its applications to financecited by 6
  3. 2019Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysiscited by 5
  4. 2018A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristicscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 9; 2026: 1.

18 papers.

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  1. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  2. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  3. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  4. 2025Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentationcited by 8
  5. 2023STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomycited by 5
  6. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  7. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  8. 2024Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directionscited by 2
  9. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  10. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  11. 2023Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encodercited by 1
  12. 2023OpenPatch: a 3D patchwork for Out-Of-Distribution detectioncited by 1
6 more
  1. 2024Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractorcited by 0
  2. 2025Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Datacited by 0
  3. 2025SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Cloudscited by 0
  4. 2025SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagationcited by 0
  5. 2025Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagationcited by 0
  6. 2026VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignmentcited by 0