Awesome

AdversarialTexture

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

Scanning Data Download

Please refer to data directory for details.

Before run following scripts, please modify the data_path in src/config.py as the absolute path of the data folder (e.g. Adversarial/data) where you download all data.

Prepare for Training (Optimization)

Please refer to src/preprocessing directory for details.

Run Training (Optimization)

Consider execute run_all.sh in parallel.

cd src/textureoptim
python gen_script.py
sh run_all.sh

Result Visualization

The result will be stored in data/result/chairID/chairID.png. You can use them to replace the corresponding default texture in data/shape, and use meshlab to open obj files to see the results.

Alternatively, we provide a simple script to render results. You will be able to see the rendering comparison in data/visual.

cd src
python visualize.py

Authors

• Jingwei Huang

© Jingwei Huang, Stanford University

IMPORTANT: If you use this code please cite the following in any resulting publication:

@inproceedings{huang2020adversarial,
  title={Adversarial Texture Optimization from RGB-D Scans},
  author={Huang, Jingwei and Thies, Justus and Dai, Angela and Kundu, Abhijit and Jiang, Chiyu and Guibas, Leonidas J and Niessner, Matthias and Funkhouser, Thomas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1559--1568},
  year={2020}
}

The rendering process is a modification of pyRender.