  We address 2D floorplan reconstruction from 3D scans. Existing approaches
typically employ heuristically designed multi-stage pipelines. Instead, we
formulate floorplan reconstruction as a single-stage structured prediction
task: find a variable-size set of polygons, which in turn are variable-length
sequences of ordered vertices. To solve it we develop a novel Transformer
architecture that generates polygons of multiple rooms in parallel, in a
holistic manner without hand-crafted intermediate stages. The model features
two-level queries for polygons and corners, and includes polygon matching to
make the network end-to-end trainable. Our method achieves a new
state-of-the-art for two challenging datasets, Structured3D and SceneCAD, along
with significantly faster inference than previous methods. Moreover, it can
readily be extended to predict additional information, i.e., semantic room
types and architectural elements like doors and windows. Our code and models
are available at: https://github.com/ywyue/RoomFormer.
