| # GIQ Benchmark |
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| ## Dataset Details |
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| GIQ is a benchmark for evaluating geometric reasoning in vision and vision-language models using polyhedra. |
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| The repository currently contains: |
| - `synthetic_images/`: synthetic renderings of polyhedra |
| - `wild_images/`: real photographs organized by source collection |
| - `3d_meshes/`: 3D mesh assets |
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| The benchmark is designed to support evaluation on tasks such as: |
| - zero-shot polyhedron classification |
| - symmetry-related reasoning |
| - mental rotation |
| - monocular 3D reconstruction |
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| ## Uses |
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| 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 |
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| ## Dataset Creation |
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| ### Curation Rationale |
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| The dataset was created to provide a controlled benchmark for geometric reasoning using polyhedra, with both synthetic renderings and real photographs. |
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| ### Source Data |
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| #### Data Collection and Processing |
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| Synthetic images were rendered from 3D polyhedron meshes. Real images were collected as photographs of physical polyhedron models. |
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| #### Who are the source data producers? |
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| The dataset authors created the synthetic portion and curated the benchmark assets. |
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| ## Personal and Sensitive Information |
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| This dataset is not intended to contain personal or sensitive information. |
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| ## Citation |
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| If you use this dataset, please cite: |
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| ```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} |
| } |
