RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment
We introduce RoTA-Bench to evaluate these abilities through interactive 3D-to-2D projection alignment.
ProofPaper ↗
Key points
- Can vision-language models turn 2D observations into 3D understanding that supports precise action?
- This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback.
- Given a target silhouette, a model observes a rotated object and chooses successive rotations to recover the target projection within a limited action budget.
- Our automated pipeline constructs silhouette-preserving 3D objects from 2D icons and uses reversible initialization to guarantee solvability, yielding 674 tasks and a stratified 100-task test set.
Sources (2)
- [1]RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection AlignmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 03:46 AM
We introduce RoTA-Bench to evaluate these abilities through interactive 3D-to-2D projection alignment.
Can vision-language models turn 2D observations into 3D understanding that supports precise action?
- [2]RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection AlignmentHugging Face Daily Papers · Oct 14, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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