GR-PSN

Dependencies

GR-PSN is implemented in PyTorch and tested with Ubuntu 20.04, please install PyTorch first following the official instruction.

• Python 3.7
• PyTorch 1.8
• numpy
• scipy
• CUDA-11.1
• RTX 3090 (24G)

For training our GR-PSN, you need download these two datasets:

Blobby shape dataset (4.7 GB), and Sculpture shape dataset (19 GB), via:

sh scripts/download_synthetic_datasets.sh

For testing our GR-PSN, you can download these datsets:

DiLiGenT main dataset (default) (850MB), via:

sh scripts/prepare_diligent_dataset.sh  

or https://drive.google.com/file/d/1EgC3x8daOWL4uQmc6c4nXVe4mdAMJVfg/view

Light Stage Data Gallery, via:

https://vgl.ict.usc.edu/Data/LightStage/

Results

We have shown some results of our GR-PSN in the document "results", including the estimations under 96 input images on the DiLiGenT benchmark dataset, and the rendered examples of object "Dragon" (.mp4).

Testing

Test on the DiLiGenT dataset

# Test GR_PSN on DiLiGenT main dataset using all of the 96 image-light pairs
python eval/run_model_Deligent.py --retrain data/models/GNet_checkp_1.pth.tar --in_img_num 96
 ``

You can change the number 96 to use an arbitrary number of input images. (1~96)

Acknowledgement:

Our code is partially based on: https://github.com/guanyingc/PS-FCN.