liygcheng / PyrResNet

For paper "Intrinsic Image Transformation Via Scale Space Decomposition"

PyrResNet

For cvpr2018 paper "Intrinsic Image Transformation Via Scale Space Decomposition" Some of the implementations are redundant and will be optimized in the future.

Prerequisites

• Linux(16.04)
• Python 2 or 3
• NVIDIA GPU(TiTan Xp) + CUDA CuDNN

Getting Started

Installation

• Install PyTorch(0.3.0) and torchvision from http://pytorch.org and other dependencies. You can install all the dependencies by

pip install -r requirements.txt

Note: The current software does not update with the newest PyTorch version, some warnings may exist.

• Clone this repo:

git clone https://github.com/liygcheng/PyrResNet.git
cd PyrResNet

• Download Sintel Dataset and MIT Dataset( and additional dataset)

https://drive.google.com/open?id=1gcNSwkDSQCwr8CezgRvL0WOX9wetlFIk

train/test

• Download dataset (e.g. sintel):
• Train a model (e.g. Scene Split):

python PyrResNet_Joint_MPI.py --cuda --niter=1000 

• To view training results and loss plots, change directory to PyrResNet/Results/ and start tensorboard as:

tensorboard --logdir=LossVis --port=10240

open the URL http://localhost:10240 and you will get the visualization results.

Citation

If you use this code for your research, please cite our papers.

@InProceedings{Cheng_2018_CVPR,
author = {Cheng, Lechao and Zhang, Chengyi and Liao, Zicheng},
title = {Intrinsic Image Transformation via Scale Space Decomposition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}