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awesome-3d-anomaly-detection

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We have summarised all 3D anomaly detection methods and datasets (still updating). 多模态，点云和姿势无关异常检测的综述仓库（持续更新）

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# Awesome 3D Anomaly Detection [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

/We discuss public datasets and related studies in detail [*updated: v4.4 2026/8/9*].

Welcome to read our paper and make comments.

We warmly welcome any new work that we may have overlooked.

⭐⭐⭐ We have made a preprint of a review available on ResearchGate, which will be updated regularly.

[3D Anomaly Detection: A Survey](https://github.com/M-3LAB/awesome-3d-anomaly-detection/blob/main/preprint_3dsurvey_.pdf) [*updated: v4.4 2026/7/21*]

![Development of 3D Anomaly Detection](https://raw.githubusercontent.com/M-3LAB/awesome-3d-anomaly-detection/main/develop_of_3AD.png)

🔥🔥🔥 Contributions to our repository are welcome. Feel free to categorize the papers and pull requests.

## Table of Contents

- [⭐ Tutorial](#-tutorial)
- [Conference Summary](#conference-summary)
  - [ICASSP2026](#a-aaai2026)
  - [CVPR2026](#a-aaai2026)
  - [AAAI2026](#a-aaai2026)
  - [ICML2026](#a-ICML2026)
- [1. Multimodal 3D Anomaly Detection](#1-multimodal-3d-anomaly-detection)
  - [1.1 Depth (2.5D) + RGB](#11-depth25drgb)
    - [1.1.1 Feature-embedding Method](#111-feature-embedding-method)
    - [1.1.2 Feature-reconstruction Method](#112-feature-reconstruction-method)
      - [1.1.2.1 U-Net Method](#1121-u-net-method)
      - [1.1.2.2 Autoencoder-based Method](#1122-autoencoder-based-method)
  - [1.2 Point Cloud + RGB](#12-point-cloudrgb)
    - [1.2.1 Feature-embedding Method](#121-feature-embedding-method)
      - [1.2.1.1 Teacher-Student Method](#1211-teacher-student-method)
      - [1.2.1.2 Memory-bank Method](#1212-memory-bank-method)
    - [1.2.2 Feature-reconstruction Method](#122-feature-reconstruction-method)
      - [1.2.2.1 Transformer-based Method](#1221-transformer-based-method)
      - [1.2.2.2 Flow-based Method](#1222-flow-based-method)
      - [1.2.2.3 GAN-based Method](#1223-gan-based-method)
      - [1.2.2.4 Autoencoder-based Method](#1224-autoencoder-based-method)
  - [1.3 Beyond Unimodal](#13-beyond-unimodal)

- [2. PointCloud 3D Anomaly Detection](#2-pointcloud-3d-anomaly-detection)
  - [2.1 Feature-reconstruction Method](#21-feature-reconstruction-method)
    - [2.1.1 Transformer-based Method](#211-transformer-based-method)
    - [2.1.2 Offset-based Method](#212-offset-based-method)
    - [2.1.3 Shape descriptor Method](#213-shape-descriptor-method)
  - [2.2 Feature-embedding Method](#22-feature-embedding-method)
    - [2.2.1 Handcrafted Mathematical Operators](#221-handcrafted-mathematical-operators)
    - [2.2.2 Deep Feature Extractors](#222-deep-feature-extractors)
    - [2.2.3 Multi-view Feature Representation](#223-multi-view-feature-representation)
  - [2.3 Zero-shot LLM Method](#23-zero-shot-llm-method)

- [3. Pose-agnostic 3D Anomaly Detection](#3-pose-agnostic-3d-anomaly-detection)
  - [3.1 Multi-view](#31-multi-view)
  - [3.2 Image-query](#32-image-query)

- [4. Datasets](#4-datasets)
  - [4.1 M3AD](#41-m3ad)
  - [4.2 PA3AD](#42-pa3ad)
  - [4.3 PC3AD](#43-pc3ad)

- [5. Others](#5-others)
- [6. BibTex Citation](#6-bibtex-citation)
- [Star History](#star-history)
- [License](#license)

---

## ⭐ Tutorial
Hi! If you are new to **3D Anomaly Detection**, this is a concise reading list and practical guidance to help you quickly understand the field. In essence, existing 3D anomaly detection methods aim to **decouple feature representations of normal and abnormal structures**, such that normal and anomalous samples exhibit different responses after being processed by a model. Based on this idea, current approaches can be broadly divided into **feature embedding–based methods** and **feature reconstruction–based methods**. It is also worth noting that low-level representations, such as raw point coordinates in point clouds, can be regarded as primitive features.

### 1. Feature Embedding–Based Methods
We recommend starting with feature embedding–based methods, as early 3D anomaly detection approaches are largely adapted from classical 2D anomaly detection models such as **PatchCore** ([paper](https://arxiv.org/abs/2106.08265)). Representative baselines include **BTF**, **M3DM**, and **Reg3D-AD**. Beginners are strongly encouraged to reproduce the following benchmark to gain hands-on understanding: **Real3D-AD** ([GitHub](https://github.com/M-3LAB/Real3D-AD)).

There are two major challenges in this paradigm. First, **insufficient feature discriminability**: 3D anomaly detection requires rotation-invariant features, or at least pose-consistent representations after transformations such as point cloud registration. This insight is well explained in **BTF** ([CVPRW 2023](https://openaccess.thecvf.com/content/CVPR2023W/VAND/papers/Horwitz_Back_to_the_Feature_Classical_3D_Features_Are_Almost_All_CVPRW_2023_paper.pdf)), and has motivated the use of **rotation-invariant deep features** ([arXiv](https://arxiv.org/abs/2510.16865)), which demonstrate strong robustness in anomaly detection. Second, **insufficient feature resolution**: many classical methods operate at the patch level. For example, **ISMP** ([arXiv](https://arxiv.org/abs/2412.13461)) and **Group3AD** ([ACM](https://dl.acm.org/doi/abs/10.1145/3664647.3680919)) assign features to only about 2% of the points. To alleviate this issue, point-level discriminative representations based on handcrafted geometric descriptors have been explored. Methods such as **CPMF** ([PR](https://www.sciencedirect.com/science/article/abs/pii/S0031320324005120)) and **Simple3AD** ([arXiv](https://arxiv.org/html/2507.07435v1)) leverage FPFH features or their variants to obtain efficient point-wise representations.

After building this understanding, the following papers are recommended for further inspiration: **Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection** ([TASE](https://ieeexplore.ieee.org/document/10898004)); **Shape-Guided Dual-Memory Learning for 3D Anomaly Detection** ([ICML 2023](https://openreview.net/forum?id=IkSGn9fcPz)); **Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory** ([AAAI 2026](https://arxiv.org/abs/2511.05966)); **CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection** ([AAAI 2026](https://arxiv.org/pdf/2511.12909), [code](https://github.com/zyh16143998882/CASL)).

### 2. Feature Reconstruction–Based Methods
With the above foundation, feature reconstruction–based methods have been further introduced. Early explorations include **IMRNet** ([GitHub](https://github.com/Chopper-233/Anomaly-ShapeNet)) and **R3D-AD** ([arXiv](https://arxiv.org/abs/2407.10862)), which remove potential anomalous structures and detect anomalies by comparing point clouds before and after reconstruction. However, feature discrimination in low-dimensional latent spaces is often limited. To address this issue, **MC3D-AD** ([arXiv](https://arxiv.org/abs/2505.01969)) performs reconstruction in a high-dimensional feature space. Nevertheless, high-dimensional reconstruction still suffers from feature resolution limitations, motivating U-Net–style architectures such as **PO3AD** ([arXiv](https://arxiv.org/abs/2412.12617)) to produce high-dimensional point-wise features.

We further recommend the following works: **G²SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection** ([ICCV 2025](https://arxiv.org/html/2503.10091v1)); **Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation** ([ICCV 2025](https://arxiv.org/abs/2505.24431), [code](https://github.com/ZZZBBBZZZ/PASDF)); **3D-SONAR: Self-Organizing Network for 3D Anomaly Ranking** ([arXiv](https://arxiv.org/pdf/2601.09294)); **C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor** ([arXiv 2025](https://arxiv.org/abs/2508.01311), [code](https://github.com/hzzzzzhappy/C3DAD)).

</details>

## Conference Summary
### ACM MM2026
1. TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection [[ACM MM 2026]](https://arxiv.org/abs/2607.11170)
   
### ECCV2026
1. CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection [[ECCV 2026]](https://arxiv.org/abs/2606.20300)
2. Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection [[ECCV 2026]](https://arxiv.org/abs/2606.29181) [[code]](https://github.com/vpc-ccg/AF3AD)
   
### ICML2026
1. Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacers [[ICML 2026]](https://icml.cc/virtual/2026/poster/62880)
2. CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection [[ICML 2026]](https://icml.cc/virtual/2026/poster/62489)
   
### ICASSP2026
1. Boosting Anomaly Detection in Industrial 3D Data: An Entropy-Guided Denoising Autoencoder [[ICASSP 2026]](https://ieeexplore.ieee.org/abstract/document/11460596)
2. 3D-Aware Semantic Alignment: Joint Global and Local Modeling for 3D Few-shot [[ICASSP 2026]](https://ieeexplore.ieee.org/abstract/document/11462177)
3. Efficient3D-AD: Token-Efficient and View-Aware Zero-shot 3D Multimodal Anomaly Detection [[ICASSP 2026]](https://ieeexplore.ieee.org/abstract/document/11465143)
   
### CVPR2026
1. Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective [[CVPR 2026]](https://arxiv.org/abs/2603.02629) [[code]](https://github.com/longkaifang/IB-IUMAD)
2. GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning [[CVPR 2026]](https://arxiv.org/abs/2602.19206) [[code]](https://github.com/zhushengxinyue/GS-CLIP)
3. Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2603.21511) [[code]](https://github.com/wistful-8029/BTP-3DAD)
4. A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2603.25159) [[code]](https://visualsciencelab-khu.github.io/SeDiR_project/)
5. Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection [[CVPR 2026 Finding]](https://arxiv.org/search/?query=3D+anomaly+detection&searchtype=all&source=header) [[code]](https://alex-costanzino.github.io/modmap/)
6. Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2604.03972) [[code]](https://github.com/alexandor91/Shape-Anomaly-Codebook)
7. Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models [[CVPR 2026 Workshop on Efficient Deep Learning for Computer Vision (ECV)]](https://arxiv.org/abs/2605.05372)
8. Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2605.07149)
9. Geometry-Aligned and Anomaly-Aware Reconstruction for 3D Anomaly Detection [[CVPR 2026]](https://cvpr.thecvf.com/virtual/2026/poster/36699)
10. Co-Adaptive Graph Learning Through Coupled Spectral Refinement for 3D Anomaly Detection [[CVPR 2026 Finding]](https://openaccess.thecvf.com/content/CVPR2026F/html/Mukkamala_Co-Adaptive_Graph_Learning_Through_Coupled_Spectral_Refinement_for_3D_Anomaly_CVPRF_2026_paper.html)
11. PTAD: Pose and Texture Agnostic Anomaly Detection [[CVPR2026 Finding]](https://openaccess.thecvf.com/content/CVPR2026F/html/Zhuo_PTAD_Pose_and_Texture_Agnostic_Anomaly_Detection_CVPRF_2026_paper.html)
12. Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View [[CVPR 2026]](https://openaccess.thecvf.com/content/CVPR2026/html/Shao_Wavelet-Driven_3D_Anomaly_Detection_under_Pose-Agnostic_and_Sparse-View_CVPR_2026_paper.html)

### AAAI2026
1. Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology [[AAAI 2026]](https://arxiv.org/abs/2511.05853v1) [[code]](https://github.com/Bingyang0410/CPS3D-Seg)
2. Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [[AAAI 2026]](https://arxiv.org/abs/2511.05966) [[code]](https://github.com/Sunny5250/CIF)
3. CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection [[AAAI 2026]](https://arxiv.org/pdf/2511.12909) [[code]](https://github.com/zyh16143998882/CASL)
   
## 1. Multimodal 3D Anomaly Detection

### 1.1 Depth(2.5D)+RGB

#### 1.1.1 Feature-embedding Method
1. **[2022]** Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [[WACV 2022]](https://arxiv.org/pdf/2210.07829.pdf)
2. **[2024]** LPFSTNet: A lightweight and parameter-free head attention-based student–teacher network for fast 3D industrial anomaly detection [[Neurocomputing]](https://www.sciencedirect.com/science/article/abs/pii/S0925231225000803)
3. **[2024]** Rethinking Reverse Distillation for Multi-Modal Anomaly Detection [[AAAI 2024]](https://ojs.aaai.org/index.php/AAAI/article/view/28687)
4. **[2025]** Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [[Arxiv]](https://realiad4ad.github.io/Real-IAD/) [[code]](https://github.com/Tencent/AnomalyDetection_Real-IAD)
5. **[2024]** Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD [[Information Fusion]](https://arxiv.org/abs/2405.13571) [[code]](https://github.com/evenrose/CMDIAD)

#### 1.1.2 Feature-reconstruction Method

##### 1.1.2.1 U-Net Method
1. **[2023]** EasyNet: An Easy Network for 3D Industrial Anomaly Detection [[ACM MM 2023]](https://arxiv.org/abs/2307.13925) [[code]](https://github.com/TaoTao9/EasyNet/blob/main/README.md)
2. **[2023]** Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data [[EIT]](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ell2.13289?af=R)
3. **[2025]** A Local Anomaly Detection Method for Occluded Targets with Multispectral Point Clouds [[IGARSS 2025]](https://ieeexplore.ieee.org/document/11243660)

##### 1.1.2.2 Autoencoder-based Method
1. **[2024]** Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [[WACV 2024]](https://openaccess.thecvf.com/content/WACV2024/papers/Zavrtanik_Cheating_Depth_Enhancing_3D_Surface_Anomaly_Detection_via_Depth_Simulation_WACV_2024_paper.pdf) [[code]](https://github.com/VitjanZ/3DSR)
2. **[2024]** Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation [[PRL 2024]](https://www.sciencedirect.com/science/article/pii/S0167865524000862)
3. **[2024]** 3D Industrial anomaly detection via dual reconstruction network [[Applied Intelligence]](https://link.springer.com/article/10.1007/s10489-024-05700-x)
4. **[2025]** Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective [[AAAI 2025]](https://arxiv.org/abs/2412.17297) [[code]](https://github.com/longkaifang/3D-ADNAS)
5. **[2025]** AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection [[CVPR 2025 SyntaGen Workshop]](https://openaccess.thecvf.com/content/CVPR2025W/SyntaGen/papers/Zhao_AnomalyHybrid_A_Domain-agnostic_Generative_Framework_for_General_Anomaly_Detection_CVPRW_2025_paper.pdf)
6. **[2025]** BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection [[MM2025]](https://arxiv.org/pdf/2507.19253) [[code]](https://github.com/Xantastic/BridgeNet)
7. **[2025]** DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2412.08949) [[code]](https://github.com/hito2448/TRD)
8. **[2026]** Dynamic background-guided asymmetric knowledge distillation network for 3D defect detection [[Journal of Manufacturing Processes]](https://www.sciencedirect.com/science/article/pii/S1526612526000824)
9. **[2026]** DCRDF-Net: A Dual-Channel Reverse-Distillation Fusion Network for 3D Industrial Anomaly Detection [[Sensors]](https://www.mdpi.com/1424-8220/26/2/412)
10. **[2026]** Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective [[CVPR 2026]](https://arxiv.org/abs/2603.02629) [[code]](https://github.com/longkaifang/IB-IUMAD)
11. **[2026]** SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2604.05632)
12. **[2026]** CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection [[ECCV 2026]](https://arxiv.org/abs/2606.20300)
13. **[2026]** DepthGate: Confidence-Gated Depth Verification for Multimodal Industrial Anomaly Detection [[ICCSA 2026]](https://link.springer.com/chapter/10.1007/978-3-032-30488-9_7)

### 1.2 Point Cloud+RGB

#### 1.2.1 Feature-embedding Method

##### 1.2.1.1 Teacher-Student Method
1. **[2022]** Anomaly detection in 3d point clouds using deep geometric descriptors [[WACV2022]](https://www.scitepress.org/Link.aspx?doi=10.5220/0010865000003124)
2. **[2022]** The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization [[VISAPP2022]](https://www.scitepress.org/Link.aspx?doi=10.5220/0010865000003124)
3. **[2024]** Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning [[PRCV2024]](https://arxiv.org/abs/2409.05378)
4. **[2024]** Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners [[Arxiv]](https://arxiv.org/abs/2412.08949) [[code]](https://github.com/hito2448/TRD)
5. **[2024]** Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation [[Arxiv]](https://arxiv.org/pdf/2405.13571)
6. **[2025]** FIND: Few-Shot Anomaly Inspection with Normal-Only Multi-Modal Data [[ICCV2025]](https://openaccess.thecvf.com/content/ICCV2025/html/Li_FIND_Few-Shot_Anomaly_Inspection_with_Normal-Only_Multi-Modal_Data_ICCV_2025_paper.html)
7. **[2025]** A method for anomaly detection of track bed based on multimodal feature fusion [[CIOP]](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13990/1399045/A-method-for-anomaly-detection-of-track-bed-based-on/10.1117/12.3088067.short)

##### 1.2.1.2 Memory-bank Method
1. **[2023]** Back to the feature: classical 3d features are (almost) all you need for 3D anomaly detection [[CVPRW2023]](https://arxiv.org/pdf/2203.05550.pdf) [[code]](https://github.com/eliahuhorwitz/3D-ADS)
2. **[2023]** Multimodal Industrial Anomaly Detection via Hybrid Fusion [[CVPR 2023]](https://arxiv.org/abs/2303.00601) [[code]](https://github.com/nomewang/M3DM)
3. **[2024]** M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising [[TPAMI]](https://arxiv.org/abs/2406.02263)
4. **[2023]** Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [[ICML 2023]](https://openreview.net/forum?id=IkSGn9fcPz)
5. **[2024]** Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2401.03145)
6. **[2024]** DAUP: Enhancing point cloud homogeneity for 3D industrial anomaly detection via density-aware point cloud upsampling [[AEI]](https://www.sciencedirect.com/science/article/pii/S1474034624004713)
7. **[2025]** VLDFNet: Views-Graph and Latent Feature Disentangled Fusion Network for Multimodal Industrial Anomaly Detection [[TIM 2025]](https://ieeexplore.ieee.org/document/11004494)
8. **[2025]** Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory [[AAAI 2026]](https://arxiv.org/abs/2511.05966) [[code]](https://github.com/Sunny5250/CIF)
9. **[2025]** Multimodal Industrial Anomaly Detection via Attention-Enhanced Memory-Guided Network [[TMM]](https://ieeexplore.ieee.org/document/11248961)
10. **[2026]** IAENet: An importance-aware ensemble model for 3D point cloud-based anomaly detection [[Information Fusion]](https://www.sciencedirect.com/science/article/pii/S1566253525011595)
11. **[2026]** Cross-Modal Disagreement-Guided Reliability-Aware Scoring for RGB-3D Industrial Anomaly Detection [[Applied Sciences]](https://www.mdpi.com/2076-3417/16/11/5483)
12. **[2026]** M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2607.13499)

#### 1.2.2 Feature-reconstruction Method

##### 1.2.2.1 Transformer-based Method
1. **[2024]** Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping [[CVPR2024]](https://arxiv.org/abs/2312.04521) [[code]](https://github.com/CVLAB-Unibo/crossmodal-feature-mapping)
2. **[2025]** Fusion-restoration model for industrial multimodal anomaly detection [[Neurocomputing]](https://www.sciencedirect.com/science/article/pii/S0925231225007453)
3. **[2025]** Multimodal Industrial Anomaly Detection via Uni-Modal and Cross-Modal Fusion [[TII]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948502)
4. **[2025]** 3D-MMFN: Multi-level Multimodal Fusion Network for 3D Industrial Image Anomaly Detection [[AEI]](https://www.sciencedirect.com/science/article/abs/pii/S1474034625001776) [[code]](https://github.com/asadmujtaba/3DMMFN)
5. **[2025]** 2D–3D Feature Fusion via Cross-Modal Latent Synthesis and Attention-Guided Restoration for Industrial Anomaly Detection [[DICTA2025]](https://arxiv.org/pdf/2510.21793) [[code]](https://github.com/adabrh/MAFR)
6. **[2025]** Unsupervised 3D Anomaly Detection Method Using Fusion of Point Cloud and Image Features [[SAFEPROCESS]](https://ieeexplore.ieee.org/document/11267808)
7. **[2025]** Multimodal Industrial Anomaly Detection via Geometric Prior [[TCSVT]](https://ieeexplore.ieee.org/document/11178073)
8. **[2025]** HGCF: Hierarchical Geometry-Color Fusion for Multimodal Industrial Anomaly Detection [[ACM MM2025]](https://dl.acm.org/doi/abs/10.1145/3746027.3754891)
9. **[2026]** Inter-modality feature prediction through multimodal fusion for 3D shape defect detection [[Neural Networks]](https://www.sciencedirect.com/science/article/pii/S0893608025009372)
10. **[2026]** A 3D anomaly detection method based on multiscale point features for defects in vehicle stamped parts [[Journal of Manufacturing Processes]](https://www.sciencedirect.com/science/article/pii/S1526612526001301#fig4)
11. **[2026]** Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2603.03939) [[code]](https://github.com/ECGAI-Research/CMDR-IAD/)
12. **[2026]** Latent Space Recovery and Disentangled Fusion for Incomplete Multimodal Industrial Anomaly Detection [[SSRN]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6379324)
13. **[2026]** Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection [[CVPR 2026 Finding]](https://arxiv.org/search/?query=3D+anomaly+detection&searchtype=all&source=header) [[code]](https://alex-costanzino.github.io/modmap/)
14. **[2026]** Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacers [[ICML 2026]](https://icml.cc/virtual/2026/poster/62880)
15. **[2026]** MaskCL: Mask-driven Cross-Modal Collaborative Learning for Multimodal Anomaly Detection [[SSRN]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6986029&__cf_chl_f_tk=7hzpyYF8uCvMIBxKvrD9iH6EvaNchxOfJDCyXQaJZqM-1782969680-1.0.1.1-J6gU_A_jwLtrTvmBOjPl.pSXo9vI9hauvvBam1dlsmU)
16. **[2026}** A Lightweight Multi-3D-Memory Extension for RGB-3D Industrial Anomaly Detection via Transformer Compression [[IEEE Access]](https://ieeexplore.ieee.org/abstract/document/11613965/authors#authors)

##### 1.2.2.2 Flow-based Method
1. **[2025]** MADFlow: Multimodal difference compensation flow for multimodal anomaly detection [[Neurocomputing]](https://www.sciencedirect.com/science/article/pii/S0925231225019150)
2. **[2025]** 2M3DF: Advancing 3D Industrial Defect Detection With Multi-Perspective Multimodal Fusion Network [[TCSVT]](https://ieeexplore.ieee.org/document/10858074)

##### 1.2.2.3 GAN-based Method
1. **[2024]** Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on Model-free Products [[ESWA]](https://arxiv.org/abs/2408.16201) [[code]](https://github.com/JiayuLiu666/Uni3DAD)

##### 1.2.2.4 Autoencoder-based Method
1. **[2024]** Incremental Template Neighborhood Matching for 3D anomaly detection [[Neurocomputing]](https://www.sciencedirect.com/science/article/abs/pii/S0925231224002546)
2. **[2025]** G$^2$SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection [[ICCV2025]](https://arxiv.org/html/2503.10091v1)
3. **[2025]** CPIR: Multimodal Industrial Anomaly Detection via Latent Bridged Cross-modal Prediction and Intra-modal Reconstruction [[AEI]](https://www.sciencedirect.com/science/article/pii/S1474034625001338)
4. **[2025]** HFMM-Net: A Hybrid Fusion Mamba Network for Efficient Multimodal Industrial Defect Detection [[Information]](https://www.mdpi.com/2078-2489/16/12/1018)
5. **[2025]** MAESTRO: A Full Point Cloud Approach for 3D Anomaly Detection Based on Reconstruction [[VISAPP2025]](https://www.scitepress.org/Link.aspx?doi=10.5220/0013250500003912)

### 1.3 Beyond Unimodal
1. **[2025]** Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [[CVPR2025]](https://arxiv.org/abs/2412.14592) [[code]](https://github.com/ZZZBBBZZZ/MulSen-AD/)
2. **[2026]** APG-Net: Adaptive Prototype Guidance Network for Multi-Sensor Industrial Anomaly Detection [[TCSVT]](https://ieeexplore.ieee.org/abstract/document/11328820)

## 2. PointCloud 3D Anomaly Detection

### 2.1 Feature-reconstruction Method

#### 2.1.1 Transformer-based Method
1. **[2021]** Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder [[ICIP2021]](https://arxiv.org/abs/2304.03420) [[code]](https://github.com/llien30/point_cloud_anomaly_detection)
2. **[2024]** Anomaly-ShapeNet: A Synthetic Dataset of Point Cloud Anomaly Detection [[CVPR2024]](https://github.com/Chopper-233/Anomaly-ShapeNet) [[code]](https://github.com/Chopper-233/Anomaly-ShapeNet)
3. **[2025]** Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection [[MM2025]](https://arxiv.org/abs/2507.03903#:~:text=In%20this%20study%2C%20a%20Down-Up%20Sampling%20Network%20%28DUS-Net%29,detection%20by%20preserving%20the%20group%20center%20geometric%20structure.)
4. **[2025]** MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection [[IJCAI2025]](https://arxiv.org/abs/2505.01969) [[code]](https://github.com/iCAN-SZU/MC3D-AD)
5. **[2025]** C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor [[Arxiv2025]](https://arxiv.org/abs/2508.01311) [[code]](https://github.com/hzzzzzhappy/C3DAD)
6. **[2025]** Anomaly-Aware Siamese Comparative Transformer for 3D Anomaly Detection [[SSRN]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5555382)
7. **[2026]** MMF-M3AD: A Unified Reconstruction Method with Multi-scale Feature Fusion for Multi-category 3D Anomaly Detection [[NN]]() [[code]](https://github.com/hzzzzzhappy/MMF-M3AD)
8. **[2026]** A 3D anomaly detection method based on multiscale point features for defects in vehicle stamped parts [[Journal of Manufacturing Processes]](https://www.sciencedirect.com/science/article/pii/S1526612526001301)
9. **[2026]** A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2603.25159) [[code]](https://visualsciencelab-khu.github.io/SeDiR_project/)
10. **[2026]** Efficient3D-AD: Token-Efficient and View-Aware Zero-shot 3D Multimodal Anomaly Detection [[ICASSP 2026]](https://ieeexplore.ieee.org/abstract/document/11465143)
11. **[2026]** From comparison to discrimination: A One-step framework for 3D anomaly detection and localization [[Image and Vision Computing]](https://www.sciencedirect.com/science/article/pii/S0262885626001393)

#### 2.1.2 Offset-based Method
1. **[2024]** R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [[ECCV2024]](https://arxiv.org/abs/2407.10862) [[code]](https://zhouzheyuan.github.io/r3d-ad)
2. **[2025]** PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection [[CVPR2025]](https://arxiv.org/abs/2412.12617) [[code]](https://github.com/yjnanan/PO3AD)
3. **[2025]** Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2505.05901) [[code]](https://github.com/hzzzzzhappy/MC4AD)
4. **[2025]** UCA-3DAL: A Unified Contrastive Framework with Test-Time Adaptation for Robust 3D Anomaly Localization [[Arxiv]](https://www.researchgate.net/publication/398446535_A_Unified_Contrastive_Framework_with_Test-Time_Adaptation_for_Robust_3D_Anomaly_Localization) [[code]](https://github.com/M-3LAB/UCA-3DL)
5. **[2026]** Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models [[CVPR 2026 Workshop on Efficient Deep Learning for Computer Vision (ECV)]](https://arxiv.org/abs/2605.05372)

#### 2.1.3 Shape descriptor Method
1. **[2023]** Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [[ICML2023]](https://openreview.net/pdf?id=IkSGn9fcPz) [[code]](https://github.com/jayliu0313/Shape-Guided)
2. **[2024]** 3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces [[Arxiv]](https://arxiv.org/abs/2404.07748)
3. **[2025]** Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation [[ICCV2025]](https://arxiv.org/abs/2505.24431) [[code]](https://github.com/ZZZBBBZZZ/PASDF)
4. **[2025]** Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2502.11307)
5. **[2025]** 3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering [[Arxiv]](https://arxiv.org/pdf/2507.13110)
6. **[2025]** 3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise [[Arxiv]](https://arxiv.org/abs/2504.12856)
7. **[2025]** Deep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data [[Arxiv]](https://arxiv.org/pdf/2502.11669)
8. **[2025]** Template3D-AD: Point Cloud Template Matching Method Based on Center Points for 3D Anomaly Detection [[IJCAI2025]](https://www.ijcai.org/proceedings/2025/0182.pdf) [[code]](https://github.com/CaedmonLY/Template3D-AD)
9. **[2025]** CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection [[AAAI 2026]](https://arxiv.org/pdf/2511.12909) [[code]](https://github.com/zyh16143998882/CASL)
10. **[2026]** 3D-SONAR: Self-Organizing Network for 3D Anomaly Ranking [[Arxiv]](https://arxiv.org/pdf/2601.09294)
11. **[2026]** Vote3D-AD: Unsupervised point cloud anomaly localization via varied defect synthesis and differentiable vote-clustering [[Alexandria Engineering Journal]](https://www.sciencedirect.com/science/article/pii/S1110016826000438)
12. **[2026]** EG3AD: An Efficient Geometry-Aware Encoding Framework for Reconstruction-Based Multi-Class Point Cloud Anomaly Detection [[Arxiv]](https://openreview.net/forum?id=Ao5WM4ZYFo)
13. **[2026]** Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2604.03972) [[code]](https://github.com/alexandor91/Shape-Anomaly-Codebook)
14. **[2026]** 3D industrial anomaly detection via local-global collaboration [[Measurement Science and Technology]](https://iopscience.iop.org/article/10.1088/1361-6501/ae83d9/meta)
15. **[2026]** SMISMP: Stable Scoring for Few-Shot 3D Anomaly Detection via Stochastic Metric Modeling [[ICIC2026]](https://dl.acm.org/doi/abs/10.1007/978-981-92-3510-0_40)

#### 2.1.4 Mamba-based Method
1. **[2026]** Multi-scale token fusion with Mamba adapters for efficient 3D industrial defect detection [[Array]](https://www.sciencedirect.com/science/article/pii/S2590005626002997)
   
### 2.2 Feature-embedding Method

#### 2.2.1 Handcrafted Mathematical Operators
1. **[2022]** Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors [[WACV 2022]](https://arxiv.org/abs/2202.11660)
2. **[2023]** Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection [[CVPRW2023]](https://openaccess.thecvf.com/content/CVPR2023W/VAND/papers/Horwitz_Back_to_the_Feature_Classical_3D_Features_Are_Almost_All_CVPRW_2023_paper.pdf) [[code]](https://github.com/eliahuhorwitz/3D-ADS/blob/main/README.md)
3. **[2024]** Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore [[Automation in Construction2025]](https://www.sciencedirect.com/science/article/pii/S0926580524005028) [[code]](https://github.com/Jingyixiong/patchcore_masonrybridge)
4. **[2024]** Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [[PR2024]](https://www.sciencedirect.com/science/article/abs/pii/S0031320324005120) [[code]](https://github.com/caoyunkang/CPMF)
5. **[2024]** PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features [[Arxiv]](https://arxiv.org/abs/2403.01804)
6. **[2025]** Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection [[ICCVW2025]](https://arxiv.org/abs/2503.01100v2)
7. **[2025]** Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects [[Arxiv]](https://arxiv.org/abs/2507.07435) [[code]](https://github.com/hustCYQ/MiniShift-Simple3D/blob/main/README.md)
8. **[2025]** 3D multimodal feature for infrastructure anomaly detection [[Automation in Construction2025]](https://arxiv.org/pdf/2502.05779) [[code]](https://github.com/Jingyixiong/3D-Multi-FPFHI)
9. **[2025]** Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data [[Arxiv]](https://arxiv.org/abs/2502.03876)
10. **[2026]** Curvature-Fused Feature Extractor for Accelerated Point Cloud Anomaly Detectio [[IEEE Access]](https://ieeexplore.ieee.org/abstract/document/11359654)
11. **[2026]** Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects [[Arxiv]](https://arxiv.org/abs/2604.01171) [[code]](https://github.com/hzzzzzhappy/open-industry/tree/main)

#### 2.2.2 Deep Feature Extractors
1. **[2022]** Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples [[Arxiv2022]](https://arxiv.org/abs/2210.17258)
2. **[2023]** Towards Total Recall in Industrial Anomaly Detection [[CVPR2023]](https://arxiv.org/abs/2106.08265) [[code]](https://github.com/M-3LAB/Real3D-AD/tree/main/patchcore)
3. **[2023]** Real3D-AD: A Dataset of Point Cloud Anomaly Detection [[NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/611b896d447df43c898062358df4c114-Abstract-Datasets_and_Benchmarks.html) [[code]](https://github.com/m-3lab/real3d-ad)
4. **[2024]** Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning [[MM2024]](https://dl.acm.org/doi/abs/10.1145/3664647.3680919)
5. **[2024]** Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection [[AAAI2025]](https://arxiv.org/abs/2412.13461) [[code]](https://github.com/M-3LAB/Look-Inside-for-More)
6. **[2025]** Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection [[TASE]](https://ieeexplore.ieee.org/document/10898004) [[code]](https://github.com/hustCYQ/GLFM-Multi-class-3DAD/blob/main/README.md)
7. **[2025]** Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection [[NIPS2025]](https://arxiv.org/abs/2510.16865) [[code]](https://github.com/CHen-ZH-W/Reg2Inv)
8. **[2025]** Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift [[Arxiv]](https://arxiv.org/abs/2503.14910)
9. **[2025]** A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features  [[PR 2026]](https://arxiv.org/abs/2511.13115) [[code]](https://github.com/hzzzzzhappy/RIF/blob/main/README.md)
10. **[2025]** Unsupervised Feature Metric-Based Multimodal Anomaly Detection Method [[CAIBDA]](https://ieeexplore.ieee.org/document/11183147)
11. **[2026]** Efficient Industrial Point Cloud Anomaly Detection via Spatial Context Aggregation and Selective Anomalous Feature Generation [[Scientific Reports]](https://www.nature.com/articles/s41598-026-41255-2)
12. **[2026]** Boosting high-resolution 3D point cloud anomaly detection with geometric constraints [[ICMV]](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/14114/3096417/Boosting-high-resolution-3D-point-cloud-anomaly-detection-with-geometric/10.1117/12.3096417.short)
13. **[2026]** Uni-DPFM: Boosting Unified 3D Point Cloud Anomaly Detection via Dual-path Feature Matching [[TMM]](https://ieeexplore.ieee.org/abstract/document/11594960)

#### 2.2.3 Multi-view Feature Representation
1. **[2024]** Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [[PR2024]](https://www.sciencedirect.com/science/article/abs/pii/S0031320324005120) [[code]](https://github.com/caoyunkang/CPMF)
2. **[2024]** Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection [[AAAI2025]](https://arxiv.org/abs/2412.13461) [[code]](https://github.com/M-3LAB/Look-Inside-for-More)
3. **[2024]** Looking 3D: Anomaly Detection with 2D-3D Alignment [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/papers/Bhunia_Looking_3D_Anomaly_Detection_with_2D-3D_Alignment_CVPR_2024_paper.pdf) [[code]](https://github.com/VICO-UoE/Looking3D)
4. **[2025]** Multi-View Reconstruction with Global Context for 3D Anomaly Detection [[IEEE SMC]](https://arxiv.org/abs/2507.21555) [[code]](https://github.com/hustSYH/MVR/blob/master/README.md)
5. **[2025]** Probing 3D Anomalies via Multi-View Registration and Dual-Residual Analysis [[SSRN]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5868807)
6. **[2026]** DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples [[Arxiv]](https://arxiv.org/pdf/2602.10806)
7. **[2026]** VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment [[Arxiv]](https://arxiv.org/abs/2606.04369)
8. **[2026]** Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2606.25740)

### 2.3 Zero-shot LLM Method
1. **[2023]** Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead [[Arxiv2023]](https://arxiv.org/abs/2311.02782)
2. **[2024]** PointAD-Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection [[NeurIPS2024]](https://proceedings.neurips.cc/paper_files/paper/2024/hash/9a263e235f6d1521d13a8531c7974951-Abstract-Conference.html) [[code]](https://github.com/zqhang/PointAD)
3. **[2024]** Towards Zero-shot 3D Anomaly Localization [[WACV2024]](https://arxiv.org/pdf/2412.04304) [[code]](https://github.com/wyzjack/3DzAL?tab=readme-ov-file)
4. **[2024]** Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework [[Arxiv]](https://arxiv.org/abs/2409.13162) [[code]](https://github.com/hustCYQ/MVP-PCLIP)
5. **[2024]** CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation [[Arxiv]](https://arxiv.org/abs/2406.18941)
6. **[2025]** Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning [[IEEE ICCSCWD2025]](https://arxiv.org/pdf/2403.11083) [[code]](https://github.com/Xiaohao-Xu/Customizable-VLM)
7. **[2025]** PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection [[Submitted to TPAMI]](https://arxiv.org/pdf/2509.03277)
8. **[2025]** MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2509.10282)
9. **[2025]** MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples [[Arxiv]](https://arxiv.org/abs/2511.10047) [[code]](https://github.com/HUST-SLOW/MuSc-V2)
10. **[2025]** On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2512.02520)
11. **[2026]** GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning [[CVPR2026]](https://arxiv.org/abs/2602.19206) [[code]](https://github.com/zhushengxinyue/GS-CLIP)
12. **[2026]** Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2603.21511) [[code]](https://github.com/wistful-8029/BTP-3DAD)
13. **[2026]** ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2604.17949)
14. **[2026]** CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection [[ICML 2026]](https://icml.cc/virtual/2026/poster/62489)
15. **[2026]** Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2605.05850)
16. **[2026]** Where & What Anomaly? A Framework for Pose-Agnostic Anomaly Detection and Zero-Shot Semantic Classification [[ICMR 2026]](https://dl.acm.org/doi/full/10.1145/3805622.3810880)
17. **[2026]** SimpleZ3D: A simple framework for zero-shot 3D industrial anomaly detection [[Pattern Recognition]](https://www.sciencedirect.com/science/article/abs/pii/S0031320326012021)

## Anomalies Synthetic
1. **[2026]** Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2604.04658) [[code]](https://github.com/hustCYQ/Synthesis4AD)
2. **[2026]** Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection [[ECCV 2026]](https://arxiv.org/abs/2606.29181) [[code]](https://github.com/vpc-ccg/AF3AD)

## 3. Pose-agnostic 3D Anomaly Detection

### 3.1 Multi-view
1. **[2025]** SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark [[ICCV2025]](https://arxiv.org/abs/2506.21549) [[code]](https://arxiv.org/abs/2506.21549)
2. **[2025]** Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [[Arxiv]](https://realiad4ad.github.io/Real-IAD/) [[code]](https://github.com/Tencent/AnomalyDetection_Real-IAD)
3. **[2026]** Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2605.07149)

### 3.2 Image-query
1. **[2023]** PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [[NeurIPS2023]](https://papers.nips.cc/paper/2023/file/8bc5aef775aacc1650a9790f1428bcea-Paper-Datasets_and_Benchmarks.pdf#:~:text=We%20introduced%20Pose-agnostic%20Anomaly%20Detection%20%28PAD%29%2C%20a%20challenging,forward%20to%20pratical%20anomaly%20detection%20and%20localization%20tasks.) [[code]](https://github.com/EricLee0224/PAD)
2. **[2024]** SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection [[CVPRW2024]](https://arxiv.org/abs/2404.06832) [[code]](https://github.com/m-kruse98/SplatPose)
3. **[2024]** SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection [[ECCVW2024]](https://arxiv.org/pdf/2404.06832) [[code]](https://github.com/Yizhe-Liu/SplatPosePlus)
4. **[2025]** PIAD: Pose and Illumination agnostic Anomaly Detection [[CVPR2025]](https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.pdf) [[code]](https://github.com/Kaichen-Yang/piad_baseline)
5. **[2026]** Pose-Agnostic Anomaly Detection with Retinex-based Illumination Alignment [[SSRN]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6055450)
6. **[2026]** 3D Gaussian Reference Parts for Robust Free-Viewpoint Visual Inspection [[IEEE Access]](https://ieeexplore.ieee.org/abstract/document/11424398/)
7. **[2026]** Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis [[Arxiv]](https://arxiv.org/abs/2603.22786) [[code]](https://chumsy0725.github.io/GS-U/)
8. **[2026]** PTAD: Pose and Texture Agnostic Anomaly Detection [[CVPR2026 Finding]](https://openaccess.thecvf.com/content/CVPR2026F/html/Zhuo_PTAD_Pose_and_Texture_Agnostic_Anomaly_Detection_CVPRF_2026_paper.html)
9. **[2026]** Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View [[CVPR 2026]](https://openaccess.thecvf.com/content/CVPR2026/html/Shao_Wavelet-Driven_3D_Anomaly_Detection_under_Pose-Agnostic_and_Sparse-View_CVPR_2026_paper.html)
10. **[2026]** Where & What Anomaly? A Framework for Pose-Agnostic Anomaly Detection and Zero-Shot Semantic Classification [[ICMR 2026]](https://dl.acm.org/doi/full/10.1145/3805622.3810880)

## 4. Datasets

### 4.1 M3AD
1. **[2022]** The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization [[VISAPP2022]](https://www.scitepress.org/Link.aspx?doi=10.5220/0010865000003124)
2. **[2022]** The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [[ACCV2022]](https://eyecan-ai.github.io/eyecandies/)
3. **[2023]** Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset [[Arxiv]](https://arxiv.org/abs/2311.04095) [[data]](https://github.com/Andy-cs008/PD-REAL)
4. **[2025]** 3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing [[Arxiv]](https://arxiv.org/abs/2507.07838)
5. **[2025]** SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark [[ICCV2025]](https://arxiv.org/abs/2506.21549) [[code]](https://arxiv.org/abs/2506.21549)
### 4.2 PA3AD
1. **[2023]** PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection [[NeurIPS2023]](https://papers.nips.cc/paper/2023/file/8bc5aef775aacc1650a9790f1428bcea-Paper-Datasets_and_Benchmarks.pdf#:~:text=We%20introduced%20Pose-agnostic%20Anomaly%20Detection%20%28PAD%29%2C%20a%20challenging,forward%20to%20pratical%20anomaly%20detection%20and%20localization%20tasks.) [[code]](https://github.com/EricLee0224/PAD)
2. **[2024]** Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection [[code]](https://realiad4ad.github.io/Real-IAD/) [[code]](https://github.com/Tencent/AnomalyDetection_Real-IAD)
3. **[2025]** PIAD: Pose and Illumination agnostic Anomaly Detection [[CVPR2025]](https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.pdf) [[code]](https://github.com/Kaichen-Yang/piad_baseline)
4. **[2025]** Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era [[Arxiv]](https://arxiv.org/abs/2511.00540)
5. **[2025]** Odd-One-Out: Anomaly Detection by Comparing with Neighbors [[CVPR 2025]](https://arxiv.org/abs/2406.20099) [[code]](https://github.com/VICO-UoE/OddOneOutAD)
### 4.3 PC3AD
1. **[2023]** Real3D-AD: A Dataset of Point Cloud Anomaly Detection [[NeurIPS2023]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/611b896d447df43c898062358df4c114-Abstract-Datasets_and_Benchmarks.html) [[code]](https://github.com/m-3lab/real3d-ad)
2. **[2024]** Anomaly-ShapeNet: A Synthetic Dataset of Point Cloud Anomaly Detection [[CVPR2024]](https://github.com/Chopper-233/Anomaly-ShapeNet) [[code]](https://github.com/Chopper-233/Anomaly-ShapeNet)
3. **[2025]** Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties [[CVPR2025]](https://arxiv.org/abs/2412.14592) [[code]](https://github.com/ZZZBBBZZZ/MulSen-AD/)
4. **[2025]** Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection [[Arxiv]](https://realiad4ad.github.io/Real-IAD/) [[code]](https://github.com/Tencent/AnomalyDetection_Real-IAD)
5. **[2025]** Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects [[Arxiv]](https://arxiv.org/html/2507.07435v1) [[code]](https://github.com/hustCYQ/MiniShift-Simple3D)
6. **[2025]** IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection [[]TCSVT](https://arxiv.org/pdf/2511.03267)
7. **[2025]** Point Cloud Segmentation of Integrated Circuits Package Substrates Surface Defects Using Causal Inference: Dataset Construction and Methodology [[AAAI 2025]](https://arxiv.org/abs/2511.05853v1)[[code]](https://github.com/Bingyang0410/CPS3D-Seg)
8. **[2026]** Auxiliary Information Flow for 3D Industrial Defect Detection on IC Ceramic Package Substrate Surfaces: Dataset and Benchmark [[TCSVT]](https://ieeexplore.ieee.org/abstract/document/11361169)[[code]](https://github.com/dcsrgh/CPS3D-Det)
9. **[2026]** Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects [[Arxiv]](https://arxiv.org/abs/2604.01171) [[code]](https://github.com/hzzzzzhappy/open-industry/tree/main)
10. **[2026]** Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection [[Arxiv]](https://arxiv.org/abs/2604.26868)
11. **[2026]** Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection [[CVPR 2026]](https://arxiv.org/abs/2605.07149)

### 4.4 Others
1. **[2026]** 3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset [[Arxiv]](https://arxiv.org/abs/2602.12820) [[code]](https://mehrn79.github.io/3DLAND/)
2. **[2026]** A Formal Mathematical Extension of the Maximum Cluster Diameter Criteria for Topologically Bounded Anomaly Detection in R 3 [[Rg]](https://www.researchgate.net/publication/406222250_A_Formal_Mathematical_Extension_of_the_Maximum_Cluster_Diameter_Criteria_for_Topologically_Bounded_Anomaly_Detection_in_R_3)

## 5. Others (Related but hard to be categorized)
1. **[2025]** ADDR: Anomaly Detection and Distortion Restoration for 3D Adversarial Point Cloud [[TIFS]](https://ieeexplore.ieee.org/document/11153519)
2. **[2026]** PointSGRADE: Sparse learning with graph representation for anomaly detection by using unstructured 3D point cloud data [[IISE Transactions]](https://www.tandfonline.com/doi/full/10.1080/24725854.2023.2285840)
3. **[2026]** Inlier-Centric Post-Training Quantization for Object Detection Models [[Arxiv]](https://arxiv.org/abs/2602.03472)
## 6. BibTex Citation

If you find our paper and repository useful, please cite our paper☺️.

```
@article{liang20253dadsurvey,
  title={3D Anomaly Detection: A Survey},
  author={Liang, Hanzhe and Guo, Bingyang and Shen, Weiming and Lyu, Jiayi and Gao, Can and Xie Guoyang  and Cao, Yunkang and Wang, Jinbao and Yu, Ruiyun and Shen, Linlin and Li, Pan},
  journal={arXiv preprint},
  doi={10.13140/RG.2.2.21218.39361},
  year={2025}
}
```

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## License
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