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shapes3d

3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth independent latent factors. These factors are floor colour, wall colour, object colour, scale, shape and orientation.

All possible combinations of these latents are present exactly once, generating N = 480000 total images.

Latent factor values

  • floor hue: 10 values linearly spaced in [0, 1]
  • wall hue: 10 values linearly spaced in [0, 1]
  • object hue: 10 values linearly spaced in [0, 1]
  • scale: 8 values linearly spaced in [0, 1]
  • shape: 4 values in [0, 1, 2, 3]
  • orientation: 15 values linearly spaced in [-30, 30]

We varied one latent at a time (starting from orientation, then shape, etc), and sequentially stored the images in fixed order in the images array. The corresponding values of the factors are stored in the same order in the labels array.

Features

FeaturesDict({
    'image': Image(shape=(64, 64, 3), dtype=tf.uint8),
    'label_floor_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'label_object_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'label_orientation': ClassLabel(shape=(), dtype=tf.int64, num_classes=15),
    'label_scale': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),
    'label_shape': ClassLabel(shape=(), dtype=tf.int64, num_classes=4),
    'label_wall_hue': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
    'value_floor_hue': Tensor(shape=[], dtype=tf.float32),
    'value_object_hue': Tensor(shape=[], dtype=tf.float32),
    'value_orientation': Tensor(shape=[], dtype=tf.float32),
    'value_scale': Tensor(shape=[], dtype=tf.float32),
    'value_shape': Tensor(shape=[], dtype=tf.float32),
    'value_wall_hue': Tensor(shape=[], dtype=tf.float32),
})

Statistics

Split Examples
TRAIN 480,000
ALL 480,000

Urls

Supervised keys (for as_supervised=True)

None

Citation

@misc{3dshapes18,
  title={3D Shapes Dataset},
  author={Burgess, Chris and Kim, Hyunjik},
  howpublished={https://github.com/deepmind/3dshapes-dataset/},
  year={2018}
}