We received Best Poster Prize at ICVSS 2019.

This is the official website of our work 3D Appearance Super-Resolution with Deep Learning (arxiv) published on CVPR2019.

We provided 3DASR, a 3D appearance SR dataset that captures both synthetic and real scenes with a large variety of texture characteristics. The dataset contains ground truth HR texture maps and LR texture maps of scaling factors ×2, ×3, and ×4. The 3D mesh, multi-view images, projection matrices, and normal maps are also provided. We introduced a deep learning-based SR framework in the multi-view setting. We showed that 2D deep learning-based SR techniques can successfully be adapted to the new texture domain by introducing the geometric information via normal maps.

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We introduce the 3DASR, a 3D appearance SR dataset and a deep learning-based approach to super-resolve the appearance of 3D objects.

Dependencies

Quick Start (Test)

  1. git clone https://github.com/ofsoundof/3D_Appearance_SR.git
  2. Download pretrained model and texture map dataset.
  3. Put pretrained model at ./experiment/.

  4. cd ./code/script

    CUDA_VISIBLE_DEVICES=xx python ../main.py --model FINETUNE --submodel NLR --save Test/NLR_first --scale 4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../../experiment/model/NLR/model_x2_split1.pt --data_train texture --data_test texture --model_one one --subset . --normal_lr lr --input_res lr --chop --reset --save_results --print_model --test_only

    Use --ext sep_reset for the first run that uses a specific split of the two splits from cross-validation.

    Be sure to change log directory --dir and data directory --dir_data.

How to Run the Code

Prepare pretrained model

  1. Download our pretrained model for 3D appearance SR from google drive or BaiduNetDisk, extraction code: nnnm. The pretrained models of NLR and NHR in the paper are included.

  2. Download the pretrained EDSR model from EDSR project page.

  3. Put the pretrained model at ./experiment.

Prepare dataset

  1. Download the texture map of the 3D appearance dataset from Google Drive or BaiduNetDisk, extraction code: crnw.

Train and test

  1. Please refer to demo.sh for the training and testing demo script. In a batch system, you can also use qsub_NLR.sh.
  2. Remember to change the log directory --dir and data directory --dir_data. --dir is the directory where you put your log information and the trained model. --dir_data is the directory where you put the dataset.

BibTeX

If you find our work useful in your research or publication, please cite our work:

Yawei Li , Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, and Luc van Gool, "3D Appearance Super-Resolution with Deep Learning" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 

@inproceedings{li2019_3dappearance,
  title={3D Appearance Super-Resolution with Deep Learning},
  author={Li, Yawei and Tsiminaki, Vagia and Timofte, Radu and Pollefeys, Marc and Van Gool, Luc},
  booktitle={In Proceedings of the IEEE International Conference on Computer Vision},
  year={2019}
}