Code for the paper: Intrinsic Image Decomposition via Ordinal Shading, Chris Careaga and Yağız Aksoy , ACM Transactions on Graphics, 2023
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In this work, we achieve high-resolution intrinsic decomposition by breaking the problem into two parts. First, we present a dense ordinal shading formulation using a shift- and scale-invariant loss in order to estimate ordinal shading cues without restricting the predictions to obey the intrinsic model. We then combine low- and high-resolution ordinal estimations using a second network to generate a shading estimate with both global coherency and local details. We encourage the model to learn an ac- curate decomposition by computing losses on the estimated shading as well as the albedo implied by the intrinsic model. We develop a straightforward method for generating dense pseudo ground truth using our model’s pre- dictions and multi-illumination data, enabling generalization to in-the-wild imagery.
Depending on how you would like to use the code in this repository there are two options to setup the code.
In either case, you should first create a fresh virtual environment (python3 -m venv intrinsic_env) and start it (source intrinsic_env/bin/activate)
If you would like to download the repository to run and make changes you can simply clone the repo:
git clone https://github.com/compphoto/Intrinsic
cd Intrinsic
then pip install all the dependencies of the repo:
pip install -r requirements.txt
Alternatively, you can install this repository as a package using setup.py:
git clone https://github.com/compphoto/Intrinsic
cd Intrinsic
python setup.py
Or perform the same action without cloning the code using:
pip install https://github.com/compphoto/Intrinsic/archive/master.zip
This will allow you to import the repository as a python package, and use our pipeline as part of your codebase.
To run our pipeline on your own images you can use the decompose script: