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. Dataset description and code is available on https://github.com/Visillect/CubePlusPlus Cube++ 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 the single or two-illuminant estimation. This makes it superior to many similar existing datasets. Also, a smaller (2GB) and easy to use version of the dataset named SimpleCube++ is provided in SimpleCube++.zip file. For a fast download please use zenodo-get. To install it use the following commands: pip install zenodo-get zenodo_get https://zenodo.org/record/4153431 --output-dir=Cube++ Please note, that PNG0.zip is missed here. To download it type the following commands cd Cube++/ wget https://storage.yandexcloud.net/cubepng0/PNG0.zip or (slower):: wget ftp://vis.iitp.ru/Cube++/PNG0.zip

{"references": ["Ershov, Egor, Alex Savchik, Illya Semenkov, Nikola Bani\u0107, Alexander Belokopytov, Daria Senshina, Karlo Koscevi\u0107, Marko Suba\u0161i\u0107, and Sven Lon\u010dari\u0107. \"The Cube++ Illumination Estimation Dataset.\" arXiv preprint arXiv:2011.10028 (2020)."]}