AIM 2020: Scene Relighting and Illumination Estimation Challenge
This work addresses the problem of scene relighting and illumination estimation for computer vision researchers, but it is incremental as it builds on existing challenge frameworks.
The paper reviewed the AIM 2020 challenge, which tackled virtual image relighting and illumination estimation using the novel VIDIT dataset, resulting in 20 confirmed submissions across three tracks with up to 94 registered participants.
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage.