A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms
Shida Beigpour, Andreas Kolb, Sven Kunz
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.
BibTeX
@inproceedings{iccv2015_acomprehensivemu,
title = {A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms},
author = {Shida Beigpour and Andreas Kolb and Sven Kunz},
booktitle = {ICCV 2015},
year = {2015}
}