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- W1986528120 abstract "Motivated by a Bayesian vision of the 3D multi-view reconstruction from images problem, we propose a dense 3D reconstruction technique that jointly refines the shape and the camera parameters of a scene by minimizing the photometric reprojection error between a generated model and the observed images, hence considering all pixels in the original images. The minimization is performed using a gradient descent scheme coherent with the shape representation (here a triangular mesh), where we derive evolution equations in order to optimize both the shape and the camera parameters. This can be used at a last refinement step in 3D reconstruction pipelines and helps improving the 3D reconstruction's quality by estimating the 3D shape and camera calibration more accurately. Examples are shown for multi-view stereo where the texture is also jointly optimized and improved, but could be used for any generative approaches dealing with multi-view reconstruction settings (ie depth map fusion, multi-view photometric stereo)." @default.
- W1986528120 created "2016-06-24" @default.
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- W1986528120 date "2014-06-01" @default.
- W1986528120 modified "2023-09-25" @default.
- W1986528120 title "Photometric Bundle Adjustment for Dense Multi-view 3D Modeling" @default.
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- W1986528120 doi "https://doi.org/10.1109/cvpr.2014.193" @default.
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