FloorplanVLM: A Vision-Language Model for Floorplan Vectorization
Converting raster floorplans into engineering-grade vector graphics is challenging due to complex topology and strict geometric constraints. To address this, we present FloorplanVLM, a unified framework that reformulates floorplan vectorization as an image-conditioned sequence modeling task. Unlike pixel-based methods that rely on fragile heuristics or query-based transformers that generate fragmented rooms, our model directly outputs structured JSON sequences representing the global topology. This 'pixels-to-sequence' paradigm enables the precise and holistic constraint satisfaction of complex geometries, such as slanted walls and curved arcs. To support this data-hungry approach, we int...
| Authors | Yuanqing Liu, Ziming Yang, Yulong Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 February 2026 |
| Last updated at source | 6 February 2026 |
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