mlsanityData to 7 Oct 2026

Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples

arXiv 2210.17258 ↗First posted 31 Oct 2022cs.CVcited by 12

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Abstract (arXiv)

Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The current mainstream methods focus on anomaly detection for images, whereas little attention has been paid to 3D point cloud. In this paper, drawing inspiration from the knowledge transfer ability of teacher-student architecture and the impressive feature extraction capability of recent neural networks, we design a teacher-student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud and then minimize a multi-scale loss between the feature vectors produced by the teacher and the student networks. Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism. Once trained, the teacher-student network pair can be leveraged jointly to fulfill 3D point cloud anomaly detection based on the calculated anomaly score. For evaluation, we compare our method against the reconstruction-based method on the ShapeNet-Part dataset. The experimental results and ablation studies quantitatively and qualitatively confirm that our model can achieve higher performance compared with the state of the arts in 3D anomaly detection with very few training samples.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 30 of its 55 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

30 of 55 references.

References · 30

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  3. 2020Knowledge Distillation: A Surveycited by 1,578
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  5. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  6. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  7. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  8. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  9. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  10. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  11. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  12. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
18 more
  1. 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
  2. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  3. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  4. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  5. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  6. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  7. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 115
  8. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  9. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  10. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  11. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  12. 2020G2D: Generate to Detect Anomalycited by 42
  13. 2020Learning from Few Samples: A Surveycited by 23
  14. 2018AVID: Adversarial Visual Irregularity Detectioncited by 19
  15. 2017Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55cited by 15
  16. 2022Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detectioncited by 11
  17. 2022Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Cloudscited by 4
  18. 2020A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signalscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 4.

12 papers.

  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. 2025Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detectioncited by 4
  4. 2023Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Datasetcited by 3
  5. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  6. 2026Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detectioncited by 3
  7. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  8. 2023Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detectioncited by 1
  9. 2026Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detectioncited by 1
  10. 2026Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detectioncited by 1
  11. 2024Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractorcited by 0
  12. 2026VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignmentcited by 0