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• W2091247382 abstract "Intrinsic images aim at separating an image into its reflectance and illumination components to facilitate further analysis or manipulation. This separation is severely ill posed and the most successful methods rely on user indications or precise geometry to resolve the ambiguities inherent to this problem. In this paper, we propose a method to estimate intrinsic images from multiple views of an outdoor scene without the need for precise geometry and with a few manual steps to calibrate the input. We use multiview stereo to automatically reconstruct a 3D point cloud of the scene. Although this point cloud is sparse and incomplete, we show that it provides the necessary information to compute plausible sky and indirect illumination at each 3D point. We then introduce an optimization method to estimate sun visibility over the point cloud. This algorithm compensates for the lack of accurate geometry and allows the extraction of precise shadows in the final image. We finally propagate the information computed over the sparse point cloud to every pixel in the photograph using image-guided propagation. Our propagation not only separates reflectance from illumination, but also decomposes the illumination into a sun, sky, and indirect layer. This rich decomposition allows novel image manipulations as demonstrated by our results." @default.
• W2091247382 created "2016-06-24" @default.
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• W2091247382 creator A5086974859 @default.
• W2091247382 date "2013-02-01" @default.
• W2091247382 modified "2023-09-25" @default.
• W2091247382 title "Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views" @default.
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• W2091247382 doi "https://doi.org/10.1109/tvcg.2012.112" @default.
• W2091247382 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22508899" @default.
• W2091247382 hasPublicationYear "2013" @default.
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