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, two challenges on illumination estimation were conducted. The main advantage of testing a method on a challenge over testing it 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 First illumination estimation challenge (IEC#1) had only a single task, global illumination estimation. The second illumination estimation challenge (IEC#2) was enriched with two additional tracks that encompassed indoor and two-illuminant illumination estimation. 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. This article 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 first and second challenge that can be useful for similar future developments.

中文翻译：

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光照估计挑战：前两年的经验

光照估计是计算色彩恒常性的关键步骤，是现代数码相机各种图像处理流程的核心部分之一。准确可靠的光照估计对于减少光照对图像颜色的影响非常重要。为了激发该领域新思想的产生和新算法的开发，进行了两个光照估计挑战。与在某些已知数据集上测试方法相比，在挑战上测试方法的主要优势在于，在提交结果之前，挑战测试图像的真实照明是未知的，这可以防止任何潜在的超参数调整可能有偏见。第一个光照估计挑战（IEC#1）只有一个任务，全局光照估计。第二个照明估计挑战 (IEC#2) 增加了两个额外的轨道，包括室内和双光源照明估计。它的其他主要特点是一个新的大型图像数据集（约 5000 张），使用相同的相机传感器模型拍摄，每张图像都有手动标记，使用 SpyderCube 提取的在许多国家/地区拍摄的场景的不同内容校准对象，以及来自 Cube++ 数据集的图像的类似竞赛的标记。本文重点介绍了过去的两个挑战，在每个赛道中获胜的算法，以及基于第一次和第二次挑战期间获得的结果得出的结论，这些结论对类似的未来发展有用。