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Description
SpatialAct data supports research on spatial reasoning tasks for architectural 3D scenes and abstract geometry. The dataset, created by Tianhui-Liu, includes tasks for spatial relation, orientation, visualization, mental rotation, error detection, and multi-turn interactive refinement. The dataset page was last updated on June 15, 2026.
Use Cases
Train models for spatial relation understanding based on 3D architectural scenes.
Benchmark mental rotation and spatial visualization algorithms using abstract geometry tasks.
Develop interactive AI agents for multi-turn refinement of spatial scenes.
Evaluate error detection and correction capabilities in spatial reasoning systems.
Strengths
Task files are organized into dedicated folders for architectural and abstract geometry scenes.
The description lists six specific spatial reasoning task types, suggesting structured content.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count and total data size are unknown, which may limit suitability assessment.
The description metadata is limited; actual data quality requires manual inspection after download.
Provenance
Source
huggingface
Collection Method
Created for the SpatialAct research project.
Freshness
Last updated 2026-06-15 02:03:49; freshness should be verified.
License is unknown; users should verify terms before use.