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• W1597060609 abstract "AbstractThis paper presents two methods for automatic 3D reconstruction: the one is a quantitative measure for frame grouping over long un-calibrated sequences, and the other is 3D reconstruction algorithm based on projective invariance. The first method evaluates the duration of corresponding points over sequence, the homography error, and the distribution of correspondences in the image. By making efficient bundles, we can overcome the limitation of the factorization, which is the assumption that all correspondences must remain in all views. In addition, we use collinearity among invariant properties in projective space to refine the projective matrix. That means any points located on the 2D imaged line must lie on the reconstructed projective line. Therefore, we regard the points unsatisfying collinearity as outliers, which are caused by a false feature tracking. After fitting a new 3D line from projective points, we iteratively obtain more precise projective matrix by using the points that are the orthogonal projection of outliers onto the line. Experimental results showed our methods can recover efficiently 3D structure from un-calibrated sequences.KeywordsProjective SpaceAugmented RealityProjective MatrixProjective ReconstructionMerging MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
• W1597060609 created "2016-06-24" @default.
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• W1597060609 date "2004-01-01" @default.
• W1597060609 modified "2023-09-26" @default.
• W1597060609 title "Two Methods for Automatic 3D Reconstruction from Long Un-calibrated Sequences" @default.
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• W1597060609 doi "https://doi.org/10.1007/978-3-540-28651-6_55" @default.
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