This is the CGIntrinsics implementation described in the paper "CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering, Z. Li and N. Snavely, ECCV 2018" .

Website: http://www.cs.cornell.edu/projects/cgintrinsics/

The code skeleton is based on "https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix" and "https://github.com/lixx2938/unsupervised-learning-intrinsic-images". If you use our code for academic purposes, please consider citing:

@inproceedings{li2018cgintrinsics,
  	title={CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering},
  	author={Zhengqi Li and Noah Snavely},
  	booktitle={European Conference on Computer Vision (ECCV)},
  	year={2018}
}

• The code was written in Pytorch 0.2 and Python 2, but it should be easy to adapt it to Python 3 version and Pytorch 0.3/0.4 if needed.

• Download the CGIntrinsics dataset (intrinsics_final) from our website: http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/intrinsics_final.zip

• Download IIW densely connected pair-wise juedgement we precomputed in our website (http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/IIW.zip) and original images and data in original IIW link (http://opensurfaces.cs.cornell.edu/publications/intrinsic/#download).

• Download SAW list in our website (http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/SAW.zip) and original data in original SAW website (https://github.com/kovibalu/saw_release).

• create a directory in your machine called CGIntrinsics

• In CGIntrinsics, you should have 3 folders (1) intrinsics_final (2) IIW (3) SAW. You should put original IIW png and json you download from http://opensurfaces.cs.cornell.edu/publications/intrinsic/#download in CGIntrinsics/IIW/data/, and you should put original data you download from https://github.com/kovibalu/saw_release in folders "CGIntrinsics/SAW/saw_images_512" and "CGIntrinsics/SAW/saw_pixel_labels" from original SAW dataset/

• In train.py file, you should root and and full_root variable to your root directory for putting CGIntrinsics folder.

• In options/base_options.py, you should change --gpu_ids to fit your machine GPUs.

• Change to "self.isTrain = True" in python file "/options/train_options.py" and run:

    python train.py

Since it seems that some people have difficulty running evaluation, we provide precomputed predictions on IIW test set and SAW test set.

• You need to download precomputed predictions for IIW test set in hdf5 format in http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/cgi_iiw.zip
• To get evalution results on IIW test set, download IIW dataset and run

    python compute_iiw_whdr.py

(you might need to change judgement_path in this python script to fit to your IIW data path)

• You need to download precomputed predictions for SAW test set in hdf5 format in http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/cgi_saw.zip
• To get evalution results on SAW test set, download SAW dataset and run

    python compute_saw_ap.py

You need modify 'full_root' in this script and to point to the SAW directory you download. To evlaute on unweighted AP% described in the paper, set 'mode = 0' in compute_saw_ap.py and to evaluate on weighted (chanllenging) AP% described in the paper, set 'mode=1' in compute_saw_ap.py.

• Note: our released model was trained on public released CGI dataset, and if you run on SAW test set, you will get 99.11% for unweighted AP% and 97.93% for weighted AP%, which is slightly better than what was described in the original ECCV camera ready paper. We have updated paper in ArXiv to reflect this change.

• Download SAW list in our website (http://www.cs.cornell.edu/projects/megadepth/dataset/cgintrinsics/SAW.zip) and original data in original SAW website (https://github.com/kovibalu/saw_release).

• Download pretrained model from http://www.cs.cornell.edu/projects/cgintrinsics/cgintrinsics_iiw_saw_final_net_G.pth and put it in "checkpoints/test_local/cgintrinsics_iiw_saw_final_net_G.pth"

• Change to "self.isTrain = False" in python file "/options/train_options.py"

• In CGIntrinsics, you should have at least 2 folders (1) IIW (2) SAW. You should put original IIW png and json you download from http://opensurfaces.cs.cornell.edu/publications/intrinsic/#download in CGIntrinsics/IIW/data/, and you should put original corresponding folders you download from https://github.com/kovibalu/saw_release in folders "CGIntrinsics/SAW/saw_images_512" and "CGIntrinsics/SAW/saw_pixel_labels".

• Change to "self.isTrain = False" in python file "/options/train_options.py"

• To run evaluation on SAW test split, change the path variable "root" and "full_root" and run:

    python test_saw.py

Note that we only compute AP% (challenge) descirbed in the paper. If you want to compute original AP%, please refer to https://github.com/lixx2938/unsupervised-learning-intrinsic-images

https://github.com/snavely/pbrs_tonemapper