10,000 spatial reasoning samples designed for geometric imagination from limited 2D visual perspectives. The dataset facilitates 3D mental modeling during reasoning tasks without the need for explicit 3D prior inputs or depth data.
Use Cases
- Train vision-language models to perform spatial reasoning using geometric imagination from 2D images
- Benchmark the ability of AI agents to perform 3D mentaling without explicit 3D data
- Develop reasoning pipelines that ground spatial logic in inferred geometric structures
Strengths
- 10,000 samples for geometric imagination grounded spatial reasoning
- Focuses on reasoning from limited 2D views rather than full 3D scenes
- Eliminates the requirement for 3D prior inputs during the reasoning process