mlsanityData to 14 Oct 2026

A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features

arXiv 2511.13115 ↗First posted 17 Nov 2025cs.CVcited by 5

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Abstract (arXiv)

3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data. However, existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly. To address this problem, we propose a novel Rotationally Invariant Features (RIF) framework for 3D AD. Firstly, to remove the adverse effect of variations on point cloud data, we develop a Point Coordinate Mapping (PCM) technique, which maps each point into a rotationally invariant space to maintain consistency of representation. Then, to learn robust and discriminative features, we design a lightweight Convolutional Transform Feature Network (CTF-Net) to extract rotationally invariant features for the memory bank. To improve the ability of the feature extractor, we introduce the idea of transfer learning to pre-train the feature extractor with 3D data augmentation. Experimental results show that the proposed method achieves the advanced performance on the Anomaly-ShapeNet dataset, with an average P-AUROC improvement of 17.7%, and also gains the best performance on the Real3D-AD dataset, with an average P-AUROC improvement of 1.6%. The strong generalization ability of RIF has been verified by combining it with traditional feature extraction methods on anomaly detection tasks, demonstrating great potential for industrial applications.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 17 Nov 2025.

Builds on

36 of 51 references.

References · 36

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  4. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  5. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  7. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  8. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  9. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 167
  10. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  11. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 142
  12. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 139
24 more
  1. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  2. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  3. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  4. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  5. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  6. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  7. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  8. 2023Collaborative Discrepancy Optimization for Reliable Image Anomaly Localizationcited by 57
  9. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  10. 2025Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detectioncited by 48
  11. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  12. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  13. 2024SplatPose & Detect: Pose-Agnostic 3D Anomaly Detectioncited by 25
  14. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  15. 2025MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detectioncited by 23
  16. 2024Continuous Memory Representation for Anomaly Detectioncited by 16
  17. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  18. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  19. 2024Looking 3D: Anomaly Detection with 2D-3D Alignmentcited by 13
  20. 2023Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worldscited by 11
  21. 2023UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detectioncited by 11
  22. 2025Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detectioncited by 11
  23. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  24. 2024OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Is Task-Specific Training Necessary for Anomaly Detection?cited by 2
  2. 2026Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detectioncited by 1
  3. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  4. 2026Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detectioncited by 0
  5. 2026MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimationcited by 0