  Illumination estimation is the essential step of computational color
constancy, one of the core parts of various image processing pipelines of
modern digital cameras. Having an accurate and reliable illumination estimation
is important for reducing the illumination influence on the image colors. To
motivate the generation of new ideas and the development of new algorithms in
this field, the 2nd Illumination estimation challenge~(IEC\#2) was conducted.
The main advantage of testing a method on a challenge over testing in on some
of the known datasets is the fact that the ground-truth illuminations for the
challenge test images are unknown up until the results have been submitted,
which prevents any potential hyperparameter tuning that may be biased.
  The challenge had several tracks: general, indoor, and two-illuminant with
each of them focusing on different parameters of the scenes. Other main
features of it are a new large dataset of images (about 5000) taken with the
same camera sensor model, a manual markup accompanying each image, diverse
content with scenes taken in numerous countries under a huge variety of
illuminations extracted by using the SpyderCube calibration object, and a
contest-like markup for the images from the Cube+ dataset that was used in
IEC\#1.
  This paper focuses on the description of the past two challenges, algorithms
which won in each track, and the conclusions that were drawn based on the
results obtained during the 1st and 2nd challenge that can be useful for
similar future developments.
