Neural Wireframe Renderer: Learning Wireframe to Image Translations

Pytorch implementation of ideas from the paper Neural Wireframe Renderer: Learning Wireframe to Image Translations by Yuan Xue, Zihan Zhou, and Xiaolei Huang

Dependencies

• Tested on CentOS 7
• Python >= 3.6
• PyTorch >= 1.0
• TensorboardX >= 1.6

Dataset

• You can download the data from here. By default, pelease extract all files inside v1.1 to the data/raw_data/imgs folder, and extract all files inside pointlines to the data/raw_data/pointlines folder.
• To preprocess the data, run

python data/preprocess.py --uni_wf

The processed data will be saved under the data folder.

Train

We support both single gpu training and multi-gpu training with Jiayuan Mao's Synchronized Batch Normalization.

Example Single GPU Training

If you are training with color guided rendering:

python train.py --gpu 0 --batch_size 14

If you are training without color guided rendering:

python train.py --gpu 0 --batch_size 14 --nocolor

Example Multiple GPU Training

python train.py --gpu 0,1,2,3 --batch_size 40

Tensorboard Visualization

tensorboard --logdir results/tb_logs/wfrenderer --port 6666

Test

Note that the --nocolor option needs to be used consistently with training. For instance, you cannot train with --nocolor and test without --nocolor.

python test.py --gpu 0 --model_path YOUR_SAVED_MODEL_PATH --out_path YOUR_OUTPUT_PATH

Input Modality

For now we only support rasterized wireframes as input, we will release the vectorized wireframe version in the near future.

Citation

We hope our implementation can serve as a baseline for wireframe rendering. If you find our work useful in your research, please consider citing:

@inproceedings{xue2020neural,
  title={Neural Wireframe Renderer: Learning Wireframe to Image Translations},
  author={Xue, Yuan and Zhou, Zihan and Huang, Xiaolei},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

Acknowledgement

Part of our code is adapted from CycleGAN. We also thank these great repos utilized in our code: LPIPS, MSSSIM, SyncBN,