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• W2015983227 abstract "We propose a technique to use the structural informa- tion extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. These novel views can be used to amplify training image collections that typically contain only a low number of views or lack certain classes of views entirely (e. g. top views). We extract the correlation of position, normal, re- flectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super- resolution and non-local smoothing." @default.
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• W2015983227 date "2014-06-01" @default.
• W2015983227 modified "2023-10-18" @default.
• W2015983227 title "Image-Based Synthesis and Re-synthesis of Viewpoints Guided by 3D Models" @default.
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• W2015983227 doi "https://doi.org/10.1109/cvpr.2014.498" @default.
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