Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

@article{Nestmeyer2017ReflectanceAF,
  title={Reflectance Adaptive Filtering Improves Intrinsic Image Estimation},
  author={Thomas Nestmeyer and Peter V. Gehler},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={1771-1780}
}
Separating an image into reflectance and shading layers poses a challenge for learning approaches because no large corpus of precise and realistic ground truth decompositions exists. The Intrinsic Images in the Wild (IIW) dataset provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images. A number of methods use IIW to learn statistical dependencies between the images and their reflectance layer. Although learning plays an important… CONTINUE READING
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