GeoState: Queryable 3D Scene State for Compositional Spatial Reasoning

Reliable 3D spatial reasoning from images requires not only accurate perception, but also representations that support explicit geometric computation. Existing vision-language models often entangle perception, geometry, and reasoning within a single language-based process. In contrast, visual-programming approaches first extract heterogeneous perception outputs such as masks, depth, and camera parameters and subsequently generate reasoning programs. We argue that this intermediate representation is a critical but underexplored design choice in visual programming. We introduce GeoState, a geometry-grounded visual-programming framework that consolidates heterogeneous monocular perception outputs into a reasoning-friendly, object-centric 3D representation. Each object is represented by its semantic identity, 3D position, orientation, and extent in a shared coordinate frame, enabling spatial relations to be computed directly through simple geometric operations. GeoState further uses a planner-guided program synthesis to decompose spatial queries into high-level geometric operations and executable code over these representations. Without task-specific spatial reasoning supervision or fine-tuning, GeoState substantially outperforms prior visual-programming approaches and achieves performance competitive with or exceeding spatially fine-tuned VLMs on stratified subsets of 3DSRBench and Omni3D-Bench. Controlled ablations further show that, with the underlying perception modules fixed, consolidating their outputs into object-centric geometry consistently improves reasoning over directly composing heterogeneous model-specific representations. These results highlight the importance of reasoning-friendly intermediate representations for reliable executable 3D spatial reasoning.