Minimal Solvers for Single-View Auto-Calibration

Lochman, Yaroslava

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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