{
  "id": "2112.09045",
  "version": "v1",
  "published": "2021-12-16T17:35:51.000Z",
  "updated": "2021-12-16T17:35:51.000Z",
  "title": "The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization",
  "authors": [
    "Paul Bergmann",
    "Xin Jin",
    "David Sattlegger",
    "Carsten Steger"
  ],
  "comment": "Accepted for presentation at VISAPP 2022",
  "doi": "10.5220/0010865000003124",
  "categories": [
    "cs.CV"
  ],
  "abstract": "We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects that manifest themselves as anomalies in the geometric structure of an object. These cause significant deviations in a 3D representation of the data. We employed a high-resolution industrial 3D sensor to acquire depth scans of 10 different object categories. For all object categories, we present a training and validation set, each of which solely consists of scans of anomaly-free samples. The corresponding test sets contain samples showing various defects such as scratches, dents, holes, contaminations, or deformations. Precise ground-truth annotations are provided for every anomalous test sample. An initial benchmark of 3D anomaly detection methods on our dataset indicates a considerable room for improvement.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-12-16T17:35:51.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "unsupervised 3d anomaly detection",
      "mvtec 3d-ad dataset",
      "sets contain samples showing",
      "localization",
      "object categories"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}