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.
- 15,000 synthetic block-stacking problems replace costly dense geometric annotation
- Covers 3D-to-2D projection, viewpoint transformation, and structural combination tasks
- Trained LVLMs beat baselines and generalize to real-world spatial tasks
- Dataset and code publicly available on GitHub
- Products
- SpatialBlockSpatialBlock-15k
From the original article
1 min read at huggingface.co
Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images -- referred to as spatial intelligence -- remains limited. Existing approaches attempt to address this gap by using real-scene spatial question answering datasets that require dense geometric annotations.
By Soohyun Ryu, Sohee Kim, Eunho Yang · huggingface.co
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