Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and
  Localization   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.

28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc