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• W2763752297 abstract "We propose a novel framework for the discretisation of multi-label problems on arbitrary, continuous domains. Our work bridges the gap between general FEM discretisations, and labeling problems that arise in a variety of computer vision tasks, including for instance those derived from the generalised Potts model. Starting from the popular formulation of labeling as a convex relaxation by functional lifting, we show that FEM discretisation is valid for the most general case, where the regulariser is anisotropic and non-metric. While our findings are generic and applicable to different vision problems, we demonstrate their practical implementation in the context of semantic 3D reconstruction, where such regularisers have proved particularly beneficial. The proposed FEM approach leads to a smaller memory footprint as well as faster computation, and it constitutes a very simple way to enable variable, adaptive resolution within the same model." @default.
• W2763752297 created "2017-10-20" @default.
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• W2763752297 date "2017-10-04" @default.
• W2763752297 modified "2023-09-23" @default.
• W2763752297 title "Semantic 3D Reconstruction with Finite Element Bases" @default.
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• W2763752297 hasPublicationYear "2017" @default.
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