Self-supervised Outdoor Scene Relighting
Ye Yu, Abhimitra Meka, Mohamed Elgharib, Hans-Peter Seidel, Christian, Theobalt, William A. P. Smith

TL;DR
This paper introduces a self-supervised method for outdoor scene relighting that learns from internet images without supervision, effectively decomposing scenes and producing realistic relighting results that generalize well.
Contribution
It presents a novel self-supervised approach that decomposes images into albedo, geometry, and illumination, enabling relighting without synthetic training data.
Findings
Produces photo-realistic relighting results
Generalizes well to unseen outdoor scenes
Does not rely on accurate geometry estimation
Abstract
Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from limited data. In contrast, we propose a self-supervised approach for relighting. Our approach is trained only on corpora of images collected from the internet without any user-supervision. This virtually endless source of training data allows training a general relighting solution. Our approach first decomposes an image into its albedo, geometry and illumination. A novel relighting is then produced by modifying the illumination parameters. Our solution capture shadow using a dedicated shadow prediction map, and does not rely on accurate geometry estimation. We evaluate our technique…
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Taxonomy
TopicsAdvanced Vision and Imaging · Computer Graphics and Visualization Techniques · Image Enhancement Techniques
