DocumentCode
3748462
Title
A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms
Author
Shida Beigpour;Andreas Kolb;Sven Kunz
Author_Institution
Dept. of Comput. Graphics &
fYear
2015
Firstpage
172
Lastpage
180
Abstract
In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.
Keywords
"Lighting","Image color analysis","Geometry","Cameras","Light sources","Complexity theory","Shape"
Publisher
ieee
Conference_Titel
Computer Vision (ICCV), 2015 IEEE International Conference on
Electronic_ISBN
2380-7504
Type
conf
DOI
10.1109/ICCV.2015.28
Filename
7410385
Link To Document