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| license: cc-by-4.0 |
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| # Dataset Card for MPI3D-realistic |
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| ## Dataset Description |
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| The **MPI3D-realistic dataset** is a **photorealistic synthetic image dataset** designed for benchmarking algorithms in **disentangled representation learning** and **unsupervised representation learning**. It is part of the broader MPI3D dataset suite, which also includes [synthetic toy](https://huggingface.co/datasets/randall-lab/mpi3d-toy), [real-world](https://huggingface.co/datasets/randall-lab/mpi3d-real) and [complex real-world](https://huggingface.co/datasets/randall-lab/mpi3d-complex) variants. |
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| The **realistic version** was rendered using a **physically-based photorealistic renderer** applied to **CAD models** of physical 3D objects. The rendering simulates realistic lighting, materials, and camera effects to closely match the real-world recordings of the MPI3D-real dataset. This enables researchers to systematically study **sim-to-real transfer** and assess how well models trained on high-fidelity synthetic images generalize to real-world data. |
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| All images depict **3D objects** under **controlled variations of 7 known factors**: |
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| - Object color (6 values) |
| - Object shape (6 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) |
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| The dataset contains **1,036,800 images** at a resolution of **64×64 pixels** (downsampled from the original resolution for benchmarking, as commonly used in the literature). All factors are **identical** to those used in the toy and real versions of MPI3D, enabling direct comparisons between different domains. |
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| ## Dataset Source |
| - **Homepage**: [https://github.com/rr-learning/disentanglement_dataset](https://github.com/rr-learning/disentanglement_dataset) |
| - **License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) |
| - **Paper**: Muhammad Waleed Gondal et al. _On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset_. NeurIPS 2019. |
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| ## Dataset Structure |
| |Factors|Possible Values| |
| |---|---| |
| |object_color|white=0, green=1, red=2, blue=3, brown=4, olive=5| |
| |object_shape|cone=0, cube=1, cylinder=2, hexagonal=3, pyramid=4, sphere=5| |
| |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| |
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| 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`). |
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| ### Why no train/test split? |
| The MPI3D-realistic dataset does not provide an official train/test split. It is designed for **representation learning research**, where the goal is to learn disentangled and interpretable latent factors. 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. |
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| ## Example Usage |
| Below is a quick example of how to load this dataset via the Hugging Face Datasets library: |
| ```python |
| from datasets import load_dataset |
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| # Load the dataset |
| dataset = load_dataset("randall-lab/mpi3d-realistic", 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 |
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| 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} |
| } |
| ``` |
