{
  "id": "2007.13215",
  "version": "v1",
  "published": "2020-07-26T20:46:41.000Z",
  "updated": "2020-07-26T20:46:41.000Z",
  "title": "OASIS: A Large-Scale Dataset for Single Image 3D in the Wild",
  "authors": [
    "Weifeng Chen",
    "Shengyi Qian",
    "David Fan",
    "Noriyuki Kojima",
    "Max Hamilton",
    "Jia Deng"
  ],
  "comment": "Accepted to CVPR 2020",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-07-26T20:46:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "single image 3d",
      "large-scale dataset",
      "single image surfaces",
      "3d vision research",
      "single-image 3d tasks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}