Ground-truth dataset and baseline evaluations for intrinsic image algorithms

R. Grosse, M.K. Johnson, E.H. Adelson and W.T. Freeman

International Conference on Computer Vision, 2009



The intrinsic image decomposition aims to retrieve “intrinsic” properties of an image, such as shading and reflectance. To make it possible to quantitatively compare different approaches to this problem in realistic settings, we present a ground-truth dataset of intrinsic image decompositions for a variety of real-world objects. For each object, we separate an image of it into three components: Lambertian shading, reflectance, and specularities.
We use our dataset to quantitatively compare several existing algorithms; we hope that this dataset will serve as a means for evaluating future work on intrinsic images.


Code and images available here.





This material is based upon work supported by the National Science Foundation under Grant No. 0739255. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.