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Repo for the papers "Intrinsic Image Decomposition via Ordinal Shading" (TOG 2023) and "Colorful Diffuse Intrinsic Image Decomposition in the Wild" (TOG 2024)
| Date | Stars |
|---|---|
| 2026-07-24 | 374 |
| 2026-07-25 | 374 |
| 2026-07-28 | 375 |
| 2026-07-30 | 375 |
| 2026-08-06 | 375 |
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# Intrinsic Image Decomposition
This repository contains the code for the following papers:
**Colorful Diffuse Intrinsic Image Decomposition in the Wild**, [Chris Careaga](https://ccareaga.github.io/) and [Yağız Aksoy](https://yaksoy.github.io), ACM Transactions on Graphics, 2024 \
[Project](https://yaksoy.github.io/ColorfulShading/) | [Paper](https://yaksoy.github.io/papers/TOG24-ColorfulShading.pdf) | [Supplementary](https://yaksoy.github.io/papers/TOG24-ColorfulShading-supp.pdf) | [Demo](https://huggingface.co/spaces/ccareaga/Intrinsic)
**Intrinsic Image Decomposition via Ordinal Shading**, [Chris Careaga](https://ccareaga.github.io/) and [Yağız Aksoy](https://yaksoy.github.io), ACM Transactions on Graphics, 2023 \
[Project](https://yaksoy.github.io/intrinsic/) | [Paper](https://yaksoy.github.io/papers/TOG23-Intrinsic.pdf) | [Video](https://www.youtube.com/watch?v=pWtJd3hqL3c) | [Supplementary](https://yaksoy.github.io/papers/TOG23-Intrinsic-Supp.pdf) | [Data](https://github.com/compphoto/MIDIntrinsics)
_Patent pending_
---
We propose a method for generating high-resolution intrinsic image decompositions for in-the-wild images. Our method consists of multiple stages. We first estimate a grayscale shading layer using our ordinal shading pipeline. We then estimate low-resolution chromaticity information to account for colorful illumination effects while maintaining global consistency. Using this initial colorful decomposition, we estimate a high-resolution, sparse albedo layer. We show that our decomposition allows us to train a diffuse shading estimation network using only a single rendered indoor dataset.
Our estimated components unlock multiple illumination-aware editing operations such as per-pixel white balancing and specularity removal:
Try out our pipeline on your own images in Colab [](https://colab.research.google.com/github/compphoto/Intrinsic/blob/main/intrinsic_inference.ipynb) or on <a href="https://huggingface.co/spaces/ccareaga/Intrinsic">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
## Setup
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`)
You can install this repository as a package using `pip`:
```
git clone https://github.com/compphoto/Intrinsic
cd Intrinsic
pip install .
```
If you want to make changes to the code and have it reflected when you import the package use `pip install --editable`
Or perform the same action without cloning the code using:
```
pip install https://github.com/compphoto/Intrinsic/archive/main.zip
```
This will allow you to import the repository as a Python package, and use our pipeline as part of your codebase.
## Inference
To run our pipeline on your own images you can use the decompose script:
```python
from chrislib.data_util import load_image
from intrinsic.pipeline import load_models, run_pipeline
# load the models from the given paths
models = load_models('v2')
# load an image (np float array in [0-1])
image = load_image('/path/to/input/image')
# run the model on the image using R_0 resizing
results = run_pipeline(models, image)
albedo = results['hr_alb']
diffuse_shading = results['dif_shd']
residual = results['residual']
# + multiple other keys for different intermediate components
```
This will run our pipeline and output the linear intrinsic components.
You can run this in your browser too! [](https://colab.research.google.com/github/compphoto/Intrinsic/blob/main/intrinsic_inference.ipynb) or <a href="https://huggingface.co/spaces/ccareaga/Intrinsic">
<img alExcerpt of 6,435 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9d834bd4ee97ffba, topic:computer-vision