INTRODUCTION

This dataset will be published in:
M. Serra, R. Benavente, M. Vanrell, O. Penacchio, and D.Samaras. Intrinsic Image Evaluation on Synthetic Complex Scenes. IEEE Journal of the Optical Society of America A (JOSA-A), In Review. (pdf)

We evaluate the following existing intrinsic image estimation algorithms on the data set:

1. X. Jiang, A. Schofield, and J. Wyatt, Correlation-based intrinsic image extraction from a single image. , in European Conference on Computer Vision (ECCV), 2010. (webpage)

2. M. Serra, O. Penacchio, R. Benavente, and M. Vanrell, Names and shades of color for intrinsic image estimation. , in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. (webpage)

3. P.V. Gehler, C. Rother, M. Kiefel, L. Zhang, and B. Schölkopf, Recovering intrinsic images with a global sparsity prior on reflectance. , in Advances in Neural Information Processing Systems (NIPS), 2011. (webpage)

4. J.T. Barron and J. Malik, Color constancy, intrinsic images, and shape estimation. , in European Conference on Computer Vision (ECCV), 2012. (webpage)

Results estimation from these algorithms can be downloaded below.

DATA

Images of the dataset: synthetic_intrisic_image_dataset.rar

CODE

Matlab code to evaluate intrinsic image algorithms: code.rar

RESULT IMAGES

Results of three intrinsic image algorithms discussed above: results.rar