This repository is an implementation of Deep Photometric Stereo Network. (http://openaccess.thecvf.com/content_ICCV_2017_workshops/w9/html/Santo_Deep_Photometric_Stereo_ICCV_2017_paper.html)
We use the deep learning framework Tensorflow with following libraries:
- Numpy
- cv2
- tqdm
- Boost.Numpy (https://github.com/ndarray/Boost.NumPy)
We use python 2.7 on Ubuntu 14.04. You can use our Dockerfile (Nvidia-docker is required).
We use following dataset for the training and evaluation.
You can download each file by download_*.sh.
DiLiGenT is only used for evaluation.
This file defines paths of each dataset and the light source directions. Now the light source directions are fit to DiLiGenT dataset. You can modify this values for your setup.
Also, the path to save the training images are defined here.
First, you need to build:
$ cd ./merl_brdf_database
$ cmake .
$ make
This is because we use BRDFRead.cpp to read MERL BRDF Database, which is the sample code in that project.
You can render synthetic training and test data by:
$ python renderin_with_merl.py
The training and test data are output to the specified path in params.py.
We use TFRecord format for training data.
You can convert rendered images to the TFRecord file by:
$ python dataset.py
$ python train.py --output_path PATH_TO_SAVE_MODEL --gpu GPU_ID
Other arguments can be confirmed by --help option.
PATH_TO_SAVE_MODEL has following directories:
Summary for tensorboard
{train|test}/cost: Output of loss function{train|test}/RMSE: Root Mean Squared Error between ground truth and predicted normal vector
Checkpoint files
Best checkpoint file. "Best" means that minimize the L_2 loss for synthetic test data.
Estimated images for synthetic test data.
Our estimated normal maps of DiLiGenT are available in .npy format.
When you want to use them for the comparison, please contact to the first author of the paper.
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