  Computational color constancy has the important task of reducing the
influence of the scene illumination on the object colors. As such, it is an
essential part of the image processing pipelines of most digital cameras. One
of the important parts of the computational color constancy is illumination
estimation, i.e. estimating the illumination color. When an illumination
estimation method is proposed, its accuracy is usually reported by providing
the values of error metrics obtained on the images of publicly available
datasets. However, over time it has been shown that many of these datasets have
problems such as too few images, inappropriate image quality, lack of scene
diversity, absence of version tracking, violation of various assumptions, GDPR
regulation violation, lack of additional shooting procedure info, etc. In this
paper, a new illumination estimation dataset is proposed that aims to alleviate
many of the mentioned problems and to help the illumination estimation
research. It consists of 4890 images with known illumination colors as well as
with additional semantic data that can further make the learning process more
accurate. Due to the usage of the SpyderCube color target, for every image
there are two ground-truth illumination records covering different directions.
Because of that, the dataset can be used for training and testing of methods
that perform single or two-illuminant estimation. This makes it superior to
many similar existing datasets. The datasets, it's smaller version
SimpleCube++, and the accompanying code are available at
https://github.com/Visillect/CubePlusPlus/.
