Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention

By Zhiliang ZENG, Xianzhi LI, Ying Kin Yu, and Chi-Wing Fu

[2021/07/26: updated download link]

[2019/08/28: updated train/test/score code & dataset]

[2019/07/29: updated demo code & pretrained model]

Introduction

This repository contains the code & annotation data for our ICCV 2019 paper: 'Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention'. In this paper, we present a new method for recognizing floor plan elements by exploring the spatial relationship between floor plan elements, model a hierarchy of floor plan elements, and design a multi-task network to learn to recognize room-boundary and room-type elements in floor plans.

Requirements

• Please install OpenCV
• Please install Python 2.7
• Please install tensorflow-gpu

Our code has been tested by using tensorflow-gpu==1.10.1 & OpenCV==3.1.0. We used Nvidia Titan Xp GPU with CUDA 9.0 installed.

Python packages

• [numpy]
• [scipy]
• [Pillow]
• [matplotlib]

Data

We share all our annotations and train-test split file here. Or download the annotation using the link in file "dataset/download_links.txt". The additional round plan is included in the annotations.

Our annotations are saved as png format. The name with suffixes "_wall.png", "_close.png" and "_room.png" are denoted "wall", "door & window" and "room types" label, respectively. We used these labels to train our multi-task network.

The name with suffixes "_close_wall.png" is the combination of "wall", "door & window" label. We don't use this label in our paper, but maybe useful for other tasks.

The name with suffixes "_multi.png" is the combination of all the labels. We used this kind of label to retrain the general segmentation network.

We also provide our training data on R3D dataset in "tfrecord" format, which can improve the loading speed during training.

To create the "tfrecord" training set, please refer to the example code in "utils/create_tfrecord.py"

All the raw floor plan image please refer to the following two links:

• R2V: https://github.com/art-programmer/FloorplanTransformation.git
• R3D: http://www.cs.toronto.edu/~fidler/projects/rent3D.html

Usage

To use our demo code, please first download the pretrained model, find the link in "pretrained/download_links.txt" file, unzip and put it into "pretrained" folder, then run

python demo.py --im_path=./demo/45719584.jpg 

To train the network, simply run

python main.py --pharse=Train

Run the following command to generate network outputs, all results are saved as png format.

python main.py --pharse=Test

To compute the evaluation metrics, please first inference the results, then simply run

python scores.py --dataset=R3D

To use our post-processing method, please first inference the results, then simply run

python postprocess.py

or

python postprocess.py --result_dir=./[result_folder_path]

Citation

If you find our work useful in your research, please consider citing:

---

@InProceedings{zlzeng2019deepfloor,
	author = {Zhiliang ZENG, Xianzhi LI, Ying Kin Yu, and Chi-Wing Fu},
	title = {Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention},
	booktitle = {IEEE International Conference on Computer Vision (ICCV)},
	year = {2019}
}

---