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• W2906006871 abstract "Abstract Plausible depth prediction from a single monocular image is a challenging task in computer vision. This paper presents a new non-parametric learning-based depth recovery framework in the gradient domain. Specifically, the proposed method leverage a global matching-based depth gradient transfer process to generate meaningful depth gradients from training images. Then, a confidence measure-based depth gradient fusion scheme is designed, which allows us to measure the individual contribution of each pixel in the warped depth gradient maps. In addition, an edge-aware depth gradient refinement strategy is proposed to mitigate the depth gradient outliers and generate accurate depth gradient values. In the end, reliable depth maps can be reconstructed based on these refined depth gradients by using the Poisson solver. Qualitative and quantitative evaluation results on the outdoor and indoor datasets demonstrate that the proposed depth estimation algorithm outperforms other data-driven methods, and is very effective for inferring convincing depth maps." @default.
• W2906006871 created "2019-01-01" @default.
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• W2906006871 date "2019-03-01" @default.
• W2906006871 modified "2023-09-24" @default.
• W2906006871 title "Depth prediction from a single image based on non-parametric learning in the gradient domain" @default.
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• W2906006871 doi "https://doi.org/10.1016/j.ijleo.2018.12.061" @default.
• W2906006871 hasPublicationYear "2019" @default.
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