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2020

VIDIT: Virtual Image Dataset for Illumination Transfer

Helou, Majed El, Zhou, Ruofan, Barthas, Johan et al.

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Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort.

Aside from aesthetic enhancement and photo montage, image relighting is valuable for domain adaptation, whether to augment datasets for training or to normalize input test data.
Accurate relighting is, however, very challenging for various reasons, such as the difficulty in removing and recasting shadows and the modeling of different surfaces.
We present a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods.

VIDIT: Virtual Image Dataset for Illumination Transfer · Around

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Bell, S., Bala, K., Snavely, N.: Intrinsic images in the wild. ACM Transactions on Graphics (TOG) 33
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Kovacs, B., Bell, S., Snavely, N., Bala, K.: Shading annotations in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6998–7007 (2017)
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Li, Z., Snavely, N.: Learning intrinsic image decomposition from watching the world. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9039–9048 (2018)
2018
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Then

Sun, T., Barron, J.T., Tsai, Y.T., Xu, Z., Yu, X., Fyffe, G., Rhemann, C., Busch, J., Debevec, P., Ramamoorthi, R.: Single image portrait relighting. ACM Transactions on Graphics (TOG) 38
2019
Later among the works it cites.
Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6849–6857 (2019)
2019
Later among the works it cites.

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