Papers › NTIRE 2021 Depth Guided Image Relighting Challenge

NTIRE 2021 Depth Guided Image Relighting Challenge

27 Apr 2021arXiv:2104.13365archive 2025-07-28

Majed El Helou, Ruofan Zhou, Sabine Susstrunk, Radu Timofte

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

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majedelhelou/VIDIT officialmentioned in papermentioned on GitHubMIT report
yazdaniamir38/Depth-guided-Image-Relighting officialmentioned in papermentioned on GitHubpytorch report

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Data AugmentationDomain AdaptationImage RelightingStyle Transfer

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Depth VIDIT

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