Intelligent Home 3D baseline on Graph2Plan data

Framework

The proposed HPGM consists of five components:

text representation block
graph conditioned layout prediction network (GC-LPN)
floor plan post-processing
language conditioned texture GAN (LCT-GAN)
3D scene generation and rendering

Figure: The overview framework of HPGM.

Dependencies

Python==3.7, PyTorch==1.8.1

Dataset

We transform the dataset of Graph2Plan to the data format of Intelligent Home 3D. We split the data following Graph2Plan as well.

Training

Train GC-LPN

python main.py --cfg cfg/layout.yml --gpu '0'

Testing

Test GC-LPN

python main.py --cfg cfg/layout_test.yml --gpu '0'

Citation

If you use any part of this code in your research, please cite the paper:

@inproceedings{chen2020intelligent,
  title={Intelligent home 3d: Automatic 3d-house design from linguistic descriptions only},
  author={Chen, Qi and Wu, Qi and Tang, Rui and Wang, Yuhan and Wang, Shuai and Tan, Mingkui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12625--12634},
  year={2020}
}