Awesome-3D-Anomaly-Detection-and-Localization

- welcome to add if any information misses. 😎

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Task Definition

Given a set of exclusively anomaly-free 3D scans of an object, the task is to detect and localize various types of anomalies

Dataset Intro

the first comprehensive dataset for unsupervised anomaly detection and localization in three-dimensional data. It consists of 4147 highresolution 3D point cloud scans from 10 real-world object categories. While the training and validation sets only contain anomaly-free data, the samples in the test set contain various types of anomalies. Precise ground truth annotations are provided for each anomaly.

Dataset Description

• Five of the object categories in our dataset exhibit considerable natural variations from sample to sample. These are bagel, carrot, cookie, peach, and potato.
• Three more objects, foam, rope, and tire, have a standardized appearance but can be easily deformed.
• The two remaining objects, cable gland and dowel, are rigid.

2023

• Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [ArXiV23]

• github repo: https://github.com/caoyunkang/CPMF

• ArXiv: https://arxiv.org/ftp/arxiv/papers/2303/2303.13194.pdf

• Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [IEEE Transactions on Industrial Informatics (TII) 2023]

• github code repo: https://github.com/caoyunkang/CDO

• Arxiv: https://arxiv.org/abs/2302.08769

• paper: https://ieeexplore.ieee.org/document/10034849

• Multimodal Industrial Anomaly Detection via Hybrid Fusion [ArXiV23]

• https://arxiv.org/pdf/2303.00601.pdf

2022

• Composite Layers for Deep Anomaly Detection on 3D Point Clouds [Arxiv22]

• https://arxiv.org/pdf/2209.11796.pdf

• A Comprehensive Real-World Photometric Stereo Dataset for Unsupervised Anomaly Detection [IEEE ACCESS]

• The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [ACCV22]

• paper:https://arxiv.org/pdf/2210.04570.pdf

• An Empirical Investigation of 3D Anomaly Detection and Segmentation [ArXiv]

• paper:https://arxiv.org/pdf/2203.05550.pdf

• Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors [Arxiv] paper:https://arxiv.org/pdf/2202.11660.pdf

• The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization [VISAPP 2022] ：

paper:https://arxiv.org/pdf/2112.09045.pdf

@misc{bergmann2021mvtec,
      title={The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization}, 
      author={Paul Bergmann and Xin Jin and David Sattlegger and Carsten Steger},
      year={2021},
      eprint={2112.09045},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Useful Links

• Paper with code:https://paperswithcode.com/paper/the-mvtec-3d-ad-dataset-for-unsupervised-3d
• MVTec3D Dateset:https://www.mvtec.com/company/research/datasets/mvtec-3d-ad
• Eyecandies Dataset:https://eyecan-ai.github.io/eyecandies

Welcome to comments and discussions!!

Xiaohao Xu: xxh11102019@outlook.com

License

This project is released under the Mit license. See LICENSE for additional details.