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Objaverse 1.0 API

Note: Objaverse 1.0's objects can be downloaded from the Objaverse-XL API, which provides more customization and support. The Objaverse 1.0 API is provided for backwards compatibility and for accessing additional annotations.

Open In Colab

Objaverse is a massive dataset of annotated 3D objects. It is hosted on 🤗Hugging Face. This notebook provides a tutorial on downloading objects and annotations!

We'll get started by downloading and importing the objaverse package from PyPi, which will allow us to easily download subsets of the dataset:

!pip install --upgrade --quiet objaverse

import objaverse
objaverse.__version__
'0.0.6'

Loading UIDs​

Each object has a unique corresponding UID (universal identifier). Let's get all the object UIDs:

uids = objaverse.load_uids()
len(uids), type(uids)
(798759, list)

Here, uids is a list of strings. Let's look at a few of them:

uids[:10]
['8476c4170df24cf5bbe6967222d1a42d',
 '8ff7f1f2465347cd8b80c9b206c2781e',
 'c786b97d08b94d02a1fa3b87d2e86cf1',
 '139331da744542009f146018fd0e05f4',
 'be2c02614d774f9da672dfdc44015219',
 'efd35e7d21ac482688c294e3b6c9f74e',
 '21d5f90dbc9f4f229b0faa7b56b67f3e',
 'dcd33159a0864de388de3a08f55e604a',
 'a7ad32b5d4d84ee5a40ebbd86da4dbe4',
 '7d6a14874eed48c2b720f0d1adfe6dd9']

Loading Annotations​

We can get the object annotations for each object using the

objaverse.load_annotations(
    uids: Optional[List[str]] = None
) -> Dict[str, Dict[str, Any]]

function. The function optionally takes in a list of the uids and returns a map of each specified UID to its corresponding annotations. If uids is not specified, the annotations for every object is returned.

annotations = objaverse.load_annotations(uids[:10])
annotations
{'8476c4170df24cf5bbe6967222d1a42d': {'uri': 'https://api.sketchfab.com/v3/models/8476c4170df24cf5bbe6967222d1a42d',
  'uid': '8476c4170df24cf5bbe6967222d1a42d',
  'name': 'Iain_Dawson_Kew_Road_Formby',
  'staffpickedAt': None,
  'viewCount': 4,
  'likeCount': 0,
  'animationCount': 0,
  'viewerUrl': 'https://sketchfab.com/3d-models/8476c4170df24cf5bbe6967222d1a42d',
  'embedUrl': 'https://sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/embed',
  'commentCount': 0,
  'isDownloadable': True,
  'publishedAt': '2021-03-18T09:36:25.430631',
  'tags': [{'name': 'stair',
    'slug': 'stair',
    'uri': 'https://api.sketchfab.com/v3/tags/stair'},
   {'name': 'staircase',
    'slug': 'staircase',
    'uri': 'https://api.sketchfab.com/v3/tags/staircase'},
   {'name': 'staircon',
    'slug': 'staircon',
    'uri': 'https://api.sketchfab.com/v3/tags/staircon'}],
  'categories': [],
  'thumbnails': {'images': [{'uid': '606cf3aaaea14bb598913e803c7b26af',
     'size': 37800,
     'width': 1920,
     'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/298948d530db40f1b783905cb20edb5d.jpeg',
     'height': 1080},
    {'uid': '143cb32654f14656b689b0ad1bd50d1b',
     'size': 11662,
     'width': 1024,
     'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/ffa34231b88440a6957f41c4e1c919ec.jpeg',
     'height': 576},
    {'uid': '91fd98a0685044fb81900e1fcf2b047c',
     'size': 6939,
     'width': 720,
     'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/180f2238f5e3444bb8362306b14c2ecc.jpeg',
     'height': 405},
    {'uid': 'f6219b24d9ff4961b52bc831f20093ee',
     'size': 1756,
     'width': 256,
     'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/366be192697549b7b73943f8c01fc100.jpeg',
     'height': 144},
    {'uid': '568c9a813b6a4131a96aaf9c038e6c70',
     'size': 550,
     'width': 64,
     'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/108ea805ed214721acc180c51d1778f8.jpeg',
     'height': 36}]},
  ...
  }

Now, if we want to look at a particular UID's annotation, we can do:

annotations[uids[0]]
{'uri': 'https://api.sketchfab.com/v3/models/8476c4170df24cf5bbe6967222d1a42d',
 'uid': '8476c4170df24cf5bbe6967222d1a42d',
 'name': 'Iain_Dawson_Kew_Road_Formby',
 'staffpickedAt': None,
 'viewCount': 4,
 'likeCount': 0,
 'animationCount': 0,
 'viewerUrl': 'https://sketchfab.com/3d-models/8476c4170df24cf5bbe6967222d1a42d',
 'embedUrl': 'https://sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/embed',
 'commentCount': 0,
 'isDownloadable': True,
 'publishedAt': '2021-03-18T09:36:25.430631',
 'tags': [{'name': 'stair',
   'slug': 'stair',
   'uri': 'https://api.sketchfab.com/v3/tags/stair'},
  {'name': 'staircase',
   'slug': 'staircase',
   'uri': 'https://api.sketchfab.com/v3/tags/staircase'},
  {'name': 'staircon',
   'slug': 'staircon',
   'uri': 'https://api.sketchfab.com/v3/tags/staircon'}],
 'categories': [],
 'thumbnails': {'images': [{'uid': '606cf3aaaea14bb598913e803c7b26af',
    'size': 37800,
    'width': 1920,
    'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/298948d530db40f1b783905cb20edb5d.jpeg',
    'height': 1080},
   {'uid': '143cb32654f14656b689b0ad1bd50d1b',
    'size': 11662,
    'width': 1024,
    'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/ffa34231b88440a6957f41c4e1c919ec.jpeg',
    'height': 576},
   {'uid': '91fd98a0685044fb81900e1fcf2b047c',
    'size': 6939,
    'width': 720,
    'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/180f2238f5e3444bb8362306b14c2ecc.jpeg',
    'height': 405},
   {'uid': 'f6219b24d9ff4961b52bc831f20093ee',
    'size': 1756,
    'width': 256,
    'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/366be192697549b7b73943f8c01fc100.jpeg',
    'height': 144},
   {'uid': '568c9a813b6a4131a96aaf9c038e6c70',
    'size': 550,
    'width': 64,
    'url': 'https://media.sketchfab.com/models/8476c4170df24cf5bbe6967222d1a42d/thumbnails/03709bf568c34654b3ce1913fbf5bd2c/108ea805ed214721acc180c51d1778f8.jpeg',
    'height': 36}]},
 'user': {'uid': 'b50b409d636f4a8e9af2111d370786bf',
  'username': 'agrice',
  'displayName': 'Alan Grice Staircase Co Ltd',
  'profileUrl': 'https://sketchfab.com/agrice',
  'account': 'basic',
  'avatar': {'uri': 'https://api.sketchfab.com/v3/avatars/d17386e905724bd7a5384abd5d2f21e3',
   'images': [{'size': 746,
     'width': 32,
     'url': 'https://media.sketchfab.com/avatars/d17386e905724bd7a5384abd5d2f21e3/339f3f93942b46b5a6ac44217789e298.jpeg',
     'height': 32},
    {'size': 1197,
     'width': 48,
     'url': 'https://media.sketchfab.com/avatars/d17386e905724bd7a5384abd5d2f21e3/6c1955e500b147298447f01354b1a0de.jpeg',
     'height': 48},
    {'size': 3174,
     'width': 90,
     'url': 'https://media.sketchfab.com/avatars/d17386e905724bd7a5384abd5d2f21e3/48fd73eced234a9ba6f94a251d53ad9b.jpeg',
     'height': 90},
    {'size': 3699,
     'width': 100,
     'url': 'https://media.sketchfab.com/avatars/d17386e905724bd7a5384abd5d2f21e3/9d25068a140f4c60b7e55cb63bd7860f.jpeg',
     'height': 100}]},
  'uri': 'https://api.sketchfab.com/v3/users/b50b409d636f4a8e9af2111d370786bf'},
 'description': 'http://staircon.com/ <br>Export by <b>Alan Grice Staircase Co. Ltd</b> (lic 6391)',
 'faceCount': 14608,
 'createdAt': '2021-03-18T09:31:52.190927',
 'vertexCount': 7309,
 'isAgeRestricted': False,
 'archives': {'glb': {'textureCount': 6,
   'size': 1546940,
   'type': 'glb',
   'textureMaxResolution': 1024,
   'faceCount': 14337,
   'vertexCount': 19008},
  'gltf': {'textureCount': 6,
   'size': 932008,
   'type': 'gltf',
   'textureMaxResolution': 1024,
   'faceCount': 14337,
   'vertexCount': 19008},
  'source': {'textureCount': None,
   'size': 1026620,
   'type': 'source',
   'textureMaxResolution': None,
   'faceCount': None,
   'vertexCount': None},
  'usdz': {'textureCount': None,
   'size': 1373035,
   'type': 'usdz',
   'textureMaxResolution': None,
   'faceCount': None,
   'vertexCount': None}},
 'license': 'by'}

Here, we see properties like the "name", "license", "description", "tags", and other metadata.

Filtering​

We can use the annotations to filter for particular properties of objects. For example, we can filter for objects that are distributed with the CC-BY license:

annotations = objaverse.load_annotations()
100%|██████████| 160/160 [01:23<00:00,  1.91it/s]

Note: Loading all annotations for the first time may take a few minutes. The annotations are being downloaded and saved locally.

And then use a list comprehension to get the CC-BY licensed objects

cc_by_uids = [uid for uid, annotation in annotations.items() if annotation["license"] == "by"]
cc_by_uids[:10]
['94db219c315742909fee67deeeacae15',
 'fc1339e225b7408caec82681be2746c5',
 '7b56f2bdfd2845588f6bde0c5362fd0d',
 '11102f046e7846b8b4053bce5779d95c',
 '963dca3a0a7b4d6caacab65165829470',
 'e406fd52136949a688d2ed5879361021',
 'f713688895dc49c7ac3d5544ec3f8387',
 'a0abc7880573486c89340e5b52b398a0',
 '898e9be8c8964f6298188a2901216e80',
 '4068b76c755349edbfac42eaa4ea34e5']

Downloading Objects​

We're going to use multiprocessing to download the objects. First, let's check our CPU count to get the number of processes we'll use:

import multiprocessing
processes = multiprocessing.cpu_count()
processes
2

For ease of demonstration, let's randomly sample 100 objects that we'll want to download:

import random

random.seed(42)

uids = objaverse.load_uids()
random_object_uids = random.sample(uids, 100)

random_object_uids
['f82039689f504922995936c68484aa61',
 'fb42332b3f5e491cb0c4b5ba7ed6f374',
 'd633b522306b4499a8e6f1ac5f517864',
 '89da97b84f4d4fbc825bf3e914d45e92',
 '32aaba5aa1954cb898111bf884404039',
 'f84c8cbd56d94be18a727c66de2c2d51',
 '6afcf8814d494372a4f3f7e3b99219ba',
 '38cb6584bb734d729baf712da41b141f',
 'f9b8b1cea04243a7bf706c4565027187',
 '306aa51bb3c24464886dce788547e6cb',
 'df4d2da4e9934b82b30f9bcab685a0fd',
 '45509feed4534fa1a34567ae82c457cf',
 'b86569c202fa455b8d221e90c5588cc7',
 '00ae6eda19634ee6b0a4a52789c9ff90',
 'bfd6abe69bde4ee7806ef3fa483b0655',
 '1b1c987aa5914a4f85eecca0a6ba9b4e',
 '7842c2ad6fe74224974d64958baea75d',
 'a018d452856f4452958e081f77cdc253',
 '9e6bd36b7b94412a9c71fdc34561a934',
 '6845849028774ca782f0e2e221c69944',
 '60a206c7952c420e844640de850f4fcf',
 'd0ca35386b0b48418f8669da1bea0923',
 '61955029fd2c48d9a6eb6c6097f93b6c',
 '329684e1e63d4d16bcf519cc9571c1fb',
 '06d70683684b4b4cbc3da27c5507ee6a',
 '71aad2b07e5a4d4bb75f4acbdb6a1aa2',
 '4954590a8ea7404f9c4a83a446313f07',
 'eac6c10064684567a054bab3824924a8',
 '37a5a43654ff4b3090d4f42a4a2f59b5',
 'e2aecaea83e840cab77384182ddea0f3',
 '20e207c435c241b392cf77b3a7507441',
 '2f9bcbdeaea74f8c8915e51da414df3c',
 '918bf1b83aa5473a849e58ff466602f5',
 'f81de979af8440f6aa578f40d26167bd',
 '2c378ccc8db7420a8a189bed03798601',
 '410293203f0f449696981afbcc4f6873',
 '8ee80065fb2144e4bce9d14cccca9ff3',
 'c701cc92af064825935891380adcad85',
 '89e3b742676c40179969772eb6879f41',
 'bf24581d4eff47c389827a444987da06',
 '0145c1e4894e450ea1812cb5af891a72',
 'fa0fd60e2ce342b3ac13d8c97ae8c43c',
 '84326404951040feb1c3571c66dc73b5',
 '95391b2c6788410b9020b43056d034d5',
 'af460cf7850a4425a56dd8bc57f97c6d',
 '2591d4d1395a4610975499df9c3bf0a8',
 '5492bdbffb5e443d8839e044de5b62de',
 '4ec6ae76dc2f4377894fbc73db2cb322',
 'dc719c32578e439b9bcde737553cbb07',
 'a4e5e0f3fd3d4af4b6dca5922784acb6',
 'e19bd5905226449ca32354d9b59302fd',
 '97938b8711724652b45b1f8c7b4b7116',
 'a4e8dccce7404907a9b56d5911078bcb',
 '7cccbc1491fd4412a347d203e2aaab4f',
 '391ecf876e944b9886178faa45933d38',
 'f7a0bf72401a4497b2e5399b87f7dc0c',
 '022c296ccade4ac7bf6dbbd04dab9342',
 'beea5043455f4831906dbb0d856b3faa',
 'f6f6006750ec47578b73bc25064b810f',
 '96ee1842cdd64f6c931f9a4fbfeff5e7',
 '3a741d3211fb4871bbf434617a8b6c0f',
 '36d4c024253d41ab8346f5109690303a',
 '3ad05cdfe560433c96d5172bc3c00852',
 '7f02efae906243f184a53997be60d9f4',
 '7f1201a69a1d4d53bac72e851b2cb1d2',
 'e6ea945cb96e44a9a426bd32a7754e29',
 '991ea3b9799d49e2b27713337c6ec4d5',
 '07b44d8694d64c4fabaf49728cea6d52',
 '69373fad6b6b4532ac303e20eb1930bd',
 '77c0d187bcac40aca483e8cc8a1dd712',
 '12c40cc1bcf644ccbc052eb1e15b0a0a',
 '4f96dd64302e4728afcdef3b7cf96a4c',
 '79506e2a4fe24cd39c1e75339ed50a8d',
 'b1076a45fd8648698dadfe36fe22cf95',
 '24384bffb5494af7b1026af3ce7130c1',
 '42ade2ed5c2f42c4904e283922b960c2',
 'd8e8a4325deb4aeabd891672dd4837a8',
 '157becd6433b41cf8784dc1b39081e67',
 '01037539e75a4f1aa01617adb05969e8',
 'ff3c3b21a1cb4b13aa9f8c40fc922bbb',
 '1cb18b2e4642443990e9fba9ac0b66ff',
 'fa172b28ffe34fd3bfd0b4cf770f7c36',
 'b880c3261f4d48c3b7648976ee358fa0',
 '87f12ed5dfa540f78c899feac2b489f6',
 '46eed325da984df1b06d012213fe0fae',
 '474c163cc4254b06b04bf2ab4697af4d',
 '57d790d4cc7d4ad19c0364ed5fd4ac06',
 '10304b3530b447c0b35a99b5ccb52eae',
 '4271089997f04cb6ae69614e18d197e4',
 'abebd5755b664106838a8f2ab4494769',
 'f1523cd935464a6098a0573bdf4fcc74',
 '1ea41f63684f40fc81ca7cf52884af76',
 'f8c2ba096c9d4b90bfa98c6749179606',
 '49658b346c9343d2bc7df008c378c5f0',
 'd679dfa07c0142a688d3ce9f33e4c265',
 '74102c99c73c4a55b90ec500b5c815b6',
 '139377d9e3704769a1355867abfc683d',
 '0c78aaf693f44865a06384e0c6a21103',
 '578a1ad692924bf1a9000b6383522acf',
 'f247cd0fd7e3452b9d8776b905d59196']

And now, let's download them with objaverse.load_objects with all of our processes. The function

objaverse.load_objects(
    uids: List[str],
    download_processes: int = 1
) -> Dict[str, str]

takes in a list of object UIDs and optionally the number of download processes, and returns a map from each object UID to its .glb file location on disk:

objects = objaverse.load_objects(
    uids=random_object_uids,
    download_processes=processes
)
objects
{'f82039689f504922995936c68484aa61': '/root/.objaverse/hf-objaverse-v1/glbs/000-120/f82039689f504922995936c68484aa61.glb',
 'fb42332b3f5e491cb0c4b5ba7ed6f374': '/root/.objaverse/hf-objaverse-v1/glbs/000-003/fb42332b3f5e491cb0c4b5ba7ed6f374.glb',
 'd633b522306b4499a8e6f1ac5f517864': '/root/.objaverse/hf-objaverse-v1/glbs/000-011/d633b522306b4499a8e6f1ac5f517864.glb',
 '89da97b84f4d4fbc825bf3e914d45e92': '/root/.objaverse/hf-objaverse-v1/glbs/000-143/89da97b84f4d4fbc825bf3e914d45e92.glb',
 '32aaba5aa1954cb898111bf884404039': '/root/.objaverse/hf-objaverse-v1/glbs/000-136/32aaba5aa1954cb898111bf884404039.glb',
 'f84c8cbd56d94be18a727c66de2c2d51': '/root/.objaverse/hf-objaverse-v1/glbs/000-110/f84c8cbd56d94be18a727c66de2c2d51.glb',
 '6afcf8814d494372a4f3f7e3b99219ba': '/root/.objaverse/hf-objaverse-v1/glbs/000-068/6afcf8814d494372a4f3f7e3b99219ba.glb',
 '38cb6584bb734d729baf712da41b141f': '/root/.objaverse/hf-objaverse-v1/glbs/000-067/38cb6584bb734d729baf712da41b141f.glb',
 'f9b8b1cea04243a7bf706c4565027187': '/root/.objaverse/hf-objaverse-v1/glbs/000-144/f9b8b1cea04243a7bf706c4565027187.glb',
 '306aa51bb3c24464886dce788547e6cb': '/root/.objaverse/hf-objaverse-v1/glbs/000-138/306aa51bb3c24464886dce788547e6cb.glb',
 'df4d2da4e9934b82b30f9bcab685a0fd': '/root/.objaverse/hf-objaverse-v1/glbs/000-024/df4d2da4e9934b82b30f9bcab685a0fd.glb',
 '45509feed4534fa1a34567ae82c457cf': '/root/.objaverse/hf-objaverse-v1/glbs/000-143/45509feed4534fa1a34567ae82c457cf.glb',
 'b86569c202fa455b8d221e90c5588cc7': '/root/.objaverse/hf-objaverse-v1/glbs/000-070/b86569c202fa455b8d221e90c5588cc7.glb',
 '00ae6eda19634ee6b0a4a52789c9ff90': '/root/.objaverse/hf-objaverse-v1/glbs/000-102/00ae6eda19634ee6b0a4a52789c9ff90.glb',
 'bfd6abe69bde4ee7806ef3fa483b0655': '/root/.objaverse/hf-objaverse-v1/glbs/000-079/bfd6abe69bde4ee7806ef3fa483b0655.glb',
 '1b1c987aa5914a4f85eecca0a6ba9b4e': '/root/.objaverse/hf-objaverse-v1/glbs/000-159/1b1c987aa5914a4f85eecca0a6ba9b4e.glb',
 '7842c2ad6fe74224974d64958baea75d': '/root/.objaverse/hf-objaverse-v1/glbs/000-090/7842c2ad6fe74224974d64958baea75d.glb',
 'a018d452856f4452958e081f77cdc253': '/root/.objaverse/hf-objaverse-v1/glbs/000-090/a018d452856f4452958e081f77cdc253.glb',
 '9e6bd36b7b94412a9c71fdc34561a934': '/root/.objaverse/hf-objaverse-v1/glbs/000-140/9e6bd36b7b94412a9c71fdc34561a934.glb',
 '6845849028774ca782f0e2e221c69944': '/root/.objaverse/hf-objaverse-v1/glbs/000-019/6845849028774ca782f0e2e221c69944.glb',
 '60a206c7952c420e844640de850f4fcf': '/root/.objaverse/hf-objaverse-v1/glbs/000-027/60a206c7952c420e844640de850f4fcf.glb',
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Once objects are downloaded locally for the first time, subsequent calls to load_objects are cached, and thus much faster:

objects = objaverse.load_objects(uids=random_object_uids)

Let's load up one of the .glb files to visualize it:

!pip install trimesh --quiet
     |████████████████████████████████| 669 kB 15.2 MB/s
[?25h
import trimesh
trimesh.load(list(objects.values())[0]).show()

NOTE: I highly recommend using Blender for rendering. It will texture the meshes much better than trimesh. I just wanted to show a quick visualization.

LVIS Annotations​

Finally, we'll load in the LVIS annotations, which returns a dictionary that maps from the LVIS category (key) to a list of object UIDs within that category (value):

lvis_annotations = objaverse.load_lvis_annotations()
lvis_annotations
{'Band_Aid': ['d4c9180a46cf401fa24fa3afe9237a43',
  '655444d3fcdc4eef9cfc993daeb4fa37',
  'dc19a68329ab435fa32344269e5a27dc',
  'e9fe70b8fc094d26a08bb4b8c1c37518',
  '6381d903bfee46dfa66e285f9717d087',
  '77f578670b59423b98e5ad9965560b9e',
  '56d77628e4f8461cb47f3bceb610397e',
  '32d5faef157f488fb146f46ec4a7ffab',
  'ea0cf48616e34708b73c82eb7c7366ca'],
 ...}

Note, too, that we have currently removed and are reviewing a few thousand of the objects that were detected as scans of humans to allow other research organizations to use the dataset.

For more information, please visit our website or open a discussion on our 🤗Hugging Face repo.