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Ground truth dataset and baseline evaluations for intrinsic image algorithms

R. Grosse, M.K. Johnson, E.H. Adelson, and W.T. Freeman, ICCV 2009


Abstract

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.

Information

title:
Ground truth dataset and baseline evaluations for intrinsic image algorithms
author:
R. Grosse,
M.K. Johnson,
E.H. Adelson,
and W.T. Freeman
citation:
Proceedings of the International Conference on Computer Vision
shortcite:
ICCV
year:
2009
created:
2010-06-02
summary:
iccv2009
tag:
grosse,
johnson,
adelson,
freeman
pdf:
http://people.csail.mit.edu/kimo
 
publications/iccv2009.1276111567.txt.gz · Last modified: 2010/06/09 16:06 (external edit)