Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention
Abstract
This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi-task neural network with two tasks: one to learn to predict room-boundary elements, and the other to predict rooms with types. More importantly, we formulate the room-boundary-guided attention mechanism in our spatial contextual module to carefully take room-boundary features into account to enhance the room-type predictions. Furthermore, we design a cross-and-within-task weighted loss to balance the multi-label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state-of-the-art methods.
Cite
Text
Zeng et al. "Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00919Markdown
[Zeng et al. "Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/zeng2019iccv-deep/) doi:10.1109/ICCV.2019.00919BibTeX
@inproceedings{zeng2019iccv-deep,
title = {{Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention}},
author = {Zeng, Zhiliang and Li, Xianzhi and Yu, Ying Kin and Fu, Chi-Wing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00919},
url = {https://mlanthology.org/iccv/2019/zeng2019iccv-deep/}
}