Code and instructions for converting rplan dataset (raster images) to housegan++ data format. House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects, CVPR 2021. Project website.
Data: RPLAN dataset, which offers 60k vector-graphics floorplans designed by professional architects.
pip install argparse
pip install numpy
pip install matplotlib
pip install shapely
pip install descartes
python raster_to_json.py --path #rplan_dataset/#image_number.png
The data file (e.g., /sample_output/0.json).
ROOM_CLASS = {"living_room": 1, "kitchen": 2, "bedroom": 3, "bathroom": 4, "balcony": 5, "entrance": 6, "dining room": 7, "study room": 8,
"storage": 10 , "front door": 15, "unknown": 16, "interior_door": 17}
# having room type in it
"room_type": [3, 4, 1, 3 ]
#bounding boxes per room
"boxes: [[72.0, 161.0, 124.0, 220.0], [72.0, 130.0, 107.0, 157.0], [111.0, 28.0, 184.0, 203.0], [72.0, 87.0, 124.0, 126.0]]
#first four entry are per list are rooms edges and 4th and 6th are showing what room type is on each side of edge
"edges":[72.0, 161.0, 72.0, 220.0, 3, 0], ...,[107.0, 130.0, 72.0, 130.0, 4, 0], [148.0, 28.0, 148.0, 87.0, 1, 2]]
#room indexes that are on each side of the edges
"ed_rm":[0], [0], [0], [0, 2], ..., [2], [2, 3], [2, 1], [2, 0], [2]]
Please consider citing our work.
@inproceedings{nauata2021house,
title={House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects},
author={Nauata, Nelson and Hosseini, Sepidehsadat and Chang, Kai-Hung and Chu, Hang and Cheng, Chin-Yi and Furukawa, Yasutaka},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13632--13641},
year={2021}
}
If you have any question, feel free to contact me at [email protected]
This research is partially supported by NSERC Discovery Grants, NSERC Discovery Grants Accelerator Supplements, DND/NSERC Discovery Grant Supplement, and Autodesk. We would like to thank architects and students for participating in our user study.