Dataset Card for MPI3D-complex

Dataset Description

The MPI3D-complex dataset is a real-world image dataset of complex everyday objects, designed for benchmarking algorithms in disentangled representation learning and robustness to object variability. It is an extension of the broader MPI3D dataset suite, which also includes synthetic toy, realistic simulated, and real-world geometric shape variants.

The complex version was recorded using the same robotic platform as the other MPI3D datasets, but with a new set of real-world objects (coffee-cup, tennis-ball, croissant, beer-cup) and a reduced color set. Images were captured using real cameras, with realistic lighting and background conditions. This allows researchers to assess how well models trained on simpler domains generalize to more complex object appearances.

All images depict real-world objects under controlled variations of 7 known factors:

• Object color (4 values)
• Object shape (4 values)
• Object size (2 values)
• Camera height (3 values)
• Background color (3 values)
• Robotic arm horizontal axis (40 values)
• Robotic arm vertical axis (40 values)

The dataset contains 460,800 images at a resolution of 64×64 pixels. The factors of variation are consistent with the other MPI3D datasets, but with different factor sizes for object shape and color.

Dataset Source

• Homepage: https://github.com/rr-learning/disentanglement_dataset
• License: Creative Commons Attribution 4.0 International
• Paper: Muhammad Waleed Gondal et al. On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset. NeurIPS 2019.

Dataset Structure

Factors	Possible Values
object_color	yellow=0, green=1, olive=2, red=3
object_shape	coffee-cup=0, tennis-ball=1, croissant=2, beer-cup=3
object_size	small=0, large=1
camera_height	top=0, center=1, bottom=2
background_color	purple=0, sea green=1, salmon=2
horizontal_axis (DOF1)	0,...,39
vertical_axis (DOF2)	0,...,39

Each image corresponds to a unique combination of these 7 factors. The images are stored in a row-major order (fastest-changing factor is vertical_axis, slowest-changing factor is object_color).

Why no train/test split?

The MPI3D-complex dataset does not provide an official train/test split. It is designed for representation learning research and for testing robustness to object complexity and appearance variation. Since the dataset is a complete Cartesian product of all factor combinations, models typically require access to the full dataset to explore factor-wise variations.

Example Usage

Below is a quick example of how to load this dataset via the Hugging Face Datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("randall-lab/mpi3d-complex", split="train", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]  # [object_color: 0, object_shape: 0, object_size: 0, camera_height: 0, background_color: 0, horizontal_axis: 0, vertical_axis: 0]
color = example["color"] # 0 
shape = example["shape"] # 0
size = example["size"] # 0
height = example["height"] # 0
background = example["background"] # 0 
dof1 = example["dof1"] # 0 
dof2 = example["dof2"] # 0

image.show()  # Display the image
print(f"Label (factors): {label}")

If you are using colab, you should update datasets to avoid errors

pip install -U datasets

Citation

@article{gondal2019transfer,
  title={On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset},
  author={Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}