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README.md

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-license: mit
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+# 3DIEBench-OOD
+
+Out-of-distribution (OOD) test set for [3DIEBench](https://github.com/facebookresearch/SIE/tree/main/data), designed to evaluate equivariance generalization to unseen rotation ranges.
+
+## Dataset Description
+
+3DIEBench-OOD contains rendered views of ShapeNet objects with rotations sampled from ranges **not seen during training**:
+
+| | Training (3DIEBench) | OOD Test (This Dataset) |
+|---|---|---|
+| **Rotation Range** | [-π/2, π/2] | [-π, -π/2] ∪ [π/2, π] |
+
+This allows testing whether models can extrapolate equivariant predictions to OOD rotation angles.
+
+For generation details, see [`data/3DIEBench/data_generate_ood.py`](https://github.com/hafezgh/seq-jepa/blob/main/seq-jepa/data/3DIEBench/data_generate_ood.py) in the seq-JEPA repository.
+
+### Structure
+
+| File | Description |
+|------|-------------|
+| `{synset}/{obj}/image_{i}.jpg` | Rendered image (256×256) |
+| `{synset}/{obj}/latent_{i}.npy` | 7D latent vector: [yaw, pitch, roll, floor_hue, spot_θ, spot_φ, spot_hue] |
+
+## Usage
+
+import numpy as np
+from PIL import Image
+
+img = Image.open('04330267/8ff18f81de484792f0b94599b4efe81/image_2.jpg')
+latent = np.load('04330267/8ff18f81de484792f0b94599b4efe81/latent_2.npy')
+# latent = [yaw, pitch, roll, floor_hue, spot_theta, spot_phi, spot_hue]## Related Resources
+
+- **seq-JEPA Code**: [GitHub](https://github.com/hafezgh/seq-jepa)
+- **Project Page**: [hafezgh.github.io/seq-jepa](https://hafezgh.github.io/seq-jepa/)
+- **Original 3DIEBench**: [SIE Repository](https://github.com/facebookresearch/SIE/tree/main/data)
+
+## Citation
+
+If you use this dataset, please cite:
+
+@inproceedings{ghaemi2025seqjepa,
+  title={seq-{JEPA}: Autoregressive Predictive Learning of Invariant-Equivariant World Models},
+  author={Ghaemi, Hafez and Muller, Eilif Benjamin and Bakhtiari, Shahab},
+  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
+  year={2025},
+  url={https://openreview.net/forum?id=GKt3VRaCU1}
+}
+
+@inproceedings{garrido2024sie,
+  title={Self-supervised learning of split invariant equivariant representations},
+  author={Garrido, Quentin and Balestriero, Randall and Najman, Laurent and LeCun, Yann},
+  booktitle={International Conference on Machine Learning},
+  year={2024}
+}