SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
Researchers release SpatialBlock-15k, a synthetic block-stacking dataset that improves 3D spatial reasoning in large vision-language models without dense geometric annotations.
The paper addresses limited spatial intelligence in LVLMs by training on structured block-manipulation tasks instead of costly real-scene annotated datasets. SpatialBlock-15k contains 15,000 synthetic problems covering 3D-to-2D projection, viewpoint transformation, and structural combination, with color modulation as visual cues. LVLMs trained on it via direct answering or reasoning-based prediction outperform baselines and generalize to real-world spatial tasks. Code and data are released on GitHub.
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