Metadata only
Date
2023Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floor-plan reconstruction as a single-stage structured prediction task: find a variablesize 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. Show more
Publication status
publishedExternal links
Book title
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
Subject
Scene analysis and understandingOrganisational unit
02219 - ETH AI Center / ETH AI Center03766 - Pollefeys, Marc / Pollefeys, Marc
03886 - Schindler, Konrad / Schindler, Konrad
More
Show all metadata
ETH Bibliography
yes
Altmetrics