Create README.md

README.md

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+# GIQ Benchmark
+
+## Dataset Details
+
+GIQ is a benchmark for evaluating geometric reasoning in vision and vision-language models using polyhedra.
+
+The repository currently contains:
+- `synthetic_images/`: synthetic renderings of polyhedra
+- `wild_images/`: real photographs organized by source collection
+- `3d_meshes/`: 3D mesh assets
+
+The benchmark is designed to support evaluation on tasks such as:
+- zero-shot polyhedron classification
+- symmetry-related reasoning
+- mental rotation
+- monocular 3D reconstruction
+
+## Uses
+
+This dataset is intended for (but not limited to):
+- benchmarking geometric reasoning in computer vision
+- evaluating robustness of foundation models on controlled 3D shape data
+- comparing synthetic and real-image performance
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to provide a controlled benchmark for geometric reasoning using polyhedra, with both synthetic renderings and real photographs.
+
+### Source Data
+
+#### Data Collection and Processing
+
+Synthetic images were rendered from 3D polyhedron meshes. Real images were collected as photographs of physical polyhedron models.
+
+#### Who are the source data producers?
+
+The dataset authors created the synthetic portion and curated the benchmark assets.
+
+## Personal and Sensitive Information
+
+This dataset is not intended to contain personal or sensitive information.
+
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{michalkiewicz2026giq,
+  title={GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra},
+  author={Michalkiewicz, Mateusz and Sokhal, Anekha and Michalkiewicz, Tadeusz and Pawlikowski, Piotr and Baktashmotlagh, Mahsa and Jampani, Varun and Balakrishnan, Guha},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026}
+}