compphoto / Intrinsic
Repo for the papers "Intrinsic Image Decomposition via Ordinal Shading" (TOG 2023) and "Colorful Diffuse Intrinsic Image Decomposition in the Wild" (TOG 2024)
Why this job: matched by what the repo talks about: image, paper, wild, repo
What it does
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.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 7 months ago
- Stars
- 390
- +3 this week
- Licence
- Custom
- Read before business use
- Downloads for
- None listed
- No release files
- Latest version
- v2.1
- Released 19 months ago
- Built with
- Python
- 70% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 1 merged
- 0 waiting
- Releases
- 3
- 0 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 70%
- Jupyter Notebook 30%
Topics
- computer-graphics
- computer-vision
- image-processing
- intrinsic-decomposition
- inverse-rendering
- machine-learning
- multi-illumination
Project record
- JobImage generation
- StatusListed
- Commits59
- Forks24
- Watching7
- Stars in 28 days+5
- CreatedNov 13, 2023
- First seen by usNov 7, 2026
- CheckedNov 8, 2026



