3DIEBench-OOD
Out-of-distribution (OOD) test set for 3DIEBench, 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 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
- Project Page: hafezgh.github.io/seq-jepa
- Original 3DIEBench: SIE Repository
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} }