- welcome to add if any information misses. 😎

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

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.

• 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.

• 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

• 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}
}

• 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

Xiaohao Xu: [email protected]

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