awesome-intrinsic-image-video-decomposition

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awesome-intrinsic-image-video-decomposition

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A collection of intrinsic image/video decomposition based on traditional methods and deep learning methods

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# awesome-intrinsic-image-video-decomposition
A collection of Intrinsic Image/Video Decomposition based on traditional methods and deep learning methods
![avatar](https://github.com/Sharanranjit/Intrinsic-Image-Decomposition/blob/master/demo.png)

###  Intrinsic Image Decomposition (Traditional method)

* _Lightness and Retinex Theory_ (Journal of the Optical Society of America 1971)[[Paper]](https://www.osapublishing.org/josa/abstract.cfm?uri=josa-61-1-1)
* _Determining Lightness from an Image_ (Computer graphics and image processing 1974)[[Paper]](http://www.cnbc.cmu.edu/~tai/cp_papers/Horn_On_Lightness_OCR.pdf)
* _Deriving intrinsic images from image sequences_ (ICCV 2001) [[Paper]](https://courses.cs.washington.edu/courses/cse590b/02au/intrinsic.pdf)
* _Intrinsic Image Decomposition with Non-Local Texture Cues_ (CVPR 2008) [[Paper]](https://www.cs.sfu.ca/~pingtan/Papers/cvpr08.pdf)
* _Intrinsic Image Decomposition Using Color Invariant Edge_ (ICIG 2009) [[Paper]](https://ieeexplore.ieee.org/document/5437858)
* _Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance_ (Advances in neural information processing systems 2011) [[Paper]](http://people.tuebingen.mpg.de/mkiefel/projects/intrinsic/nips11intrinsic.pdf)
* _Intrinsic Images Using Optimization_ (CVPR 2011) [[Paper]](https://ieeexplore.ieee.org/document/5995507)
* _Shape, Albedo, and Illumination from a Single Image of an Unknown Object_ (CVPR 2012) [[Paper]](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/reconstruction/BarronMalikCVPR2012.pdf)
* _Intrinsic scene properties from a single RGB-D image_ (CVPR 2013) [[Paper]](https://projet.liris.cnrs.fr/imagine/pub/proceedings/CVPR-2013/data/papers/4989a017.pdf)
* _Self-Supervised Intrinsic Image Decomposition_ (NIPS 2013) [[Paper]](https://arxiv.org/abs/1711.03678)[[Code]](https://github.com/JannerM/intrinsics-network)
* _Shape, illumination, and reflectance from shading_ (TPAMI 2014) [[Paper]](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-117.pdf)
* _Intrinsic Images in the Wild_ (TOG 2014) [[Project]](http://opensurfaces.cs.cornell.edu/intrinsic/)
* _Learning Lightness from Human Judgement on Relative Reflectance_ (CVPR 2015) [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.698.5035&rep=rep1&type=pdf)
* _An L 1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition_ (SIGGRAPH 2015) [[Paper]](http://cseweb.ucsd.edu/~bisai/papers/SIGGRAPH15_IntrinsicDecomposition.pdf)
* _Reflectance Adaptive Filtering Improves Intrinsic Image Estimation_ (CVPR 2017) [[Paper]](https://arxiv.org/abs/1612.05062)

### Intrinsic Video Decomposition (Traditional method)
* _Intrinsic Video and Applications_ (TOG 2014) [[Project]](http://webdiis.unizar.es/~elenag/projects/SIG2014_intrinsicvideo/)
* _Interactive intrinsic video editing_ (TOG 2014)[[Project]](https://perso.liris.cnrs.fr/nicolas.bonneel/intrinsic.htm)
* _Blind video temporal consistency_ (TOG 2015) [[Project]](https://perso.liris.cnrs.fr/nicolas.bonneel/consistency/)
* _Live Intrinsic Video_ (TOG 2016 [[Project]](https://gvv.mpi-inf.mpg.de/projects/LiveIntrinsicVideo/)
* _Re-texturing by intrinsic video_ (DICTA 2019) [[Papre]](https://ieeexplore.ieee.org/document/5692608)

### Intrinsic Image Decomposition (Deep learning method)
* _Learning Lightness from Human Judgement on Relative Reflectance_ (CVPR 2015)[[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.698.5035&rep=rep1&type=pdf)
* _Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition_ (ICCV 2015)[[Paper]](https://people.eecs.berkeley.edu/~tinghuiz/papers/iccv15_lrp.pdf)
* _Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression_ (ICCV 2015) [[Paper]](https://arxiv.org/abs/1512.02311)
* _Scene Intrinsics and Depth from a Single Image_ (ICCV 2015) [[Paper]](http://www.icsi.berkeley.edu/pubs/vision/sceneintrinsics15.pdf)
* _Learning Ordinal Relationships for Mid-Level Vision_ (ICCV 2015) [[Paper]](https://people.csail.mit.edu/danielzoran/ordinal.pdf)
* _Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture_ (ICCV 2015) [[Paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Eigen_Predicting_Depth_Surface_ICCV_2015_paper.pdf)
* _Learning Non-Lambertian Object Intrinsics across ShapeNet Categories_ (CVPR 2017) [[Paper]](https://arxiv.org/abs/1612.08510)
* _Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks_ (CVPR 2017) [[Paper]](https://arxiv.org/abs/1612.07429)
* _CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering_ (ECCV 2018) [[Project]](https://research.cs.cornell.edu/cgintrinsics/)
* _Revisiting Deep Intrinsic Image Decompositions_ (CVPR 2018) [[Paper]](https://arxiv.org/abs/1701.02965)
* _SfSNet: Learning Shape, Reflectance and Illuminance of Faces ‘in the wild’_ (CVPR  2018) [[Paper]](https://arxiv.org/abs/1712.01261)
* _Intrinsic Image Transformation via Scale Space Decomposition_ (CVPR  2018) [[Paper]](https://arxiv.org/abs/1805.10253)
* _Learning intrinsic image decomposition from watching the world_ (CVPR 2018) [[Paper]](https://arxiv.org/abs/1804.00582)
* _InverseRenderNet: Learning single image inverse rendering_ (CVPR 2019) [[Paper]](https://arxiv.org/abs/1811.12328)
* _Unsupervised Learning for Intrinsic Image Decomposition from a Single Image_ (CVPR 2020) [[Paper]](https://arxiv.org/abs/1911.09930)

### Intrinsic Video Decomposition (Deep learning method)
* _Learning Blind Video Temporal Consistency_ (ECCV 2018) [[Project]](https://arxiv.org/abs/1808.00449)[[code]](https://github.com/phoenix104104/fast_blind_video_consistency)

### Datasets

* _MIT Intrinsic Image Dataset_[[Data]](http://www.cs.toronto.edu/~rgrosse/intrinsic/)
* _Intrinsic Image in the Wild (IIW) Dataset_[[Data]](http://opensurfaces.cs.cornell.edu/publications/intrinsic/)
* _MPI Sintel Dataset_[[Data]](http://sintel.is.tue.mpg.de/)
* _DAVIS (Densely Annotated VIdeo Segmentation)_[[Data]](https://davischallenge.org/)

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