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Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).
Python C++ Cuda Shell CMake
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Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

AdversarialTexture Teaser

Scanning Data Download

Please refer to data directory for details.

Before run following scripts, please modify the data_path in src/ 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 in parallel.

cd src/textureoptim

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


© Jingwei Huang, Stanford University

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

  title={Adversarial Texture Optimization from RGB-D Scans},
  author={Huang, Jingwei and Thies, Justus and Dai, Angela and Kundu, Abhijit and Jiang, Chiyu Max and Guibas, Leonidas and Nie{\ss}ner, Matthias and Funkhouser, Thomas},
  journal={arXiv preprint arXiv:2003.08400},

The rendering process is a modification of pyRender.

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