Minimal Solvers for Single-View Auto-Calibration

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dc.contributor.author Lochman, Yaroslava
dc.date.accessioned 2020-02-21T15:45:30Z
dc.date.available 2020-02-21T15:45:30Z
dc.date.issued 2020
dc.identifier.citation Lochman, Yaroslava. Minimal Solvers for Single-View Auto-Calibration : Master Thesis : manuscript rights / Orest Kupyn ; Supervisors James Pritts, Oles Dobosevych, Rostyslav Hryniv ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2020. – 57 p. : ill. uk
dc.identifier.uri http://er.ucu.edu.ua/handle/1/2039
dc.language.iso en uk
dc.subject Perspective Projection uk
dc.subject Homography Decomposition uk
dc.subject Vanishing Point Estimation uk
dc.title Minimal Solvers for Single-View Auto-Calibration uk
dc.type Preprint uk
dc.status Публікується вперше uk
dc.description.abstracten We introduce a new hybrid minimal solver that admits combinations of radially-distorted conjugate translations and radially-distorted parallel lines from the common scene plane to jointly estimate lens undistortion and affine rectification. The solver is the first to admit complementary geometric primitives for rectification purposes. In addition, a novel solver admitting three pairs of imaged parallel scene lines for the same problem is introduced. The proposed solvers are used with the Manhattan scene assumption to auto-calibrate cameras from a single image. Thesolversaregeneratedusingelementarymethodsfromalgebraicgeometry. As a result, they are simple, fast and robust. The solvers are used in an adaptive sampling framework that favors the feature combinations that are most frequently consistent with accurate scene plane rectifications. Auto-calibrations are recovered from challenging images that have either a sparsity of scene lines or scene texture. The method is fully automatic. uk


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