3dcompat.yaml

Name: "3DCoMPaT: Composition of Materials on Parts of 3D Things"
Description: |
  3D CoMPaT is a richly annotated large-scale dataset of rendered compositions of Materials on Parts of thousands of unique 3D Models.
  This dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials.
  Each object with the applied part-material compositions is rendered from four equally spaced views as well as four randomized views.
  We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects.
  We present two variations of this task and adapt state-of-art 2D/3D deep learning methods to solve the problem as baselines for future research.
  We hope our work will help ease future research on compositional 3D Vision.


Documentation: https://3dcompat-dataset.org/
Contact: mohamed.elhoseiny@kaust.edu.sa, yuchen.li@kaust.edu.sa
ManagedBy: "[Vision-CAIR, CEMSE, KAUST](https://cemse.kaust.edu.sa/vision-cair)"
UpdateFrequency: Continually improving 3D annotations and renderings
Tags:
  - aws-pds
  - computer vision
  - machine learning
License: |
  https://3dcompat-dataset.org/LICENSE
Resources:
  - Description: 3DCoMPaT Dataset
    ARN: arn:aws:s3:::3dcompat-dataset
    Region: us-west-1
    Type: S3 Bucket
    Explore:
      - "[Website](https://3dcompat-dataset.org/)"
DataAtWork:
  Publications:
    - Title: "3DCoMPaT: Composition of Materials on Parts of 3D Things"
      URL: https://3dcompat-dataset.org/pdf/paper.pdf
      AuthorName: Yuchen Li, Ujjwal Upadhyay, Habib Slim, Ahmed Abdelreheem, Arpit Prajapati, Suhail Pothigara, Peter Wonka & Mohamed Elhoseiny