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- W4220778841 abstract "In this paper, we propose a dense depth estimation pipeline for multiview 360∘ images. The proposed pipeline leverages a spherical camera model that compensates for radial distortion in 360∘ images. The key contribution of this paper is the extension of a spherical camera model to multiview by introducing a translation scaling scheme. Moreover, we propose an effective dense depth estimation method by setting virtual depth and minimizing photonic reprojection error. We validate the performance of the proposed pipeline using the images of natural scenes as well as the synthesized dataset for quantitive evaluation. The experimental results verify that the proposed pipeline improves estimation accuracy compared to the current state-of-art dense depth estimation methods." @default.
- W4220778841 created "2022-04-03" @default.
- W4220778841 creator A5056079132 @default.
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- W4220778841 date "2022-03-08" @default.
- W4220778841 modified "2023-09-27" @default.
- W4220778841 title "Dense depth estimation from multiple 360-degree images using virtual depth" @default.
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- W4220778841 doi "https://doi.org/10.1007/s10489-022-03391-w" @default.
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