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2019 | OriginalPaper | Buchkapitel

Weak-Perspective and Scaled-Orthographic Structure from Motion with Missing Data

verfasst von : Levente Hajder

Erschienen in: Computer Vision, Imaging and Computer Graphics – Theory and Applications

Verlag: Springer International Publishing

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Abstract

Perspective n-Point (PnP) problem is in focus of 3D computer vision community since the late 80’s. Standard solutions deal with the pinhole camera model, the problem is challenging due to the perspectivity. The well-known PnP algorithms assume that the intrinsic camera parameters are known, therefore, only extrinsic ones are needed to estimate. It is carried out by a rough estimation, usually given in closed forms, then the accurate camera parameters are obtained via numerical optimization. In this paper, we show that both the weak-perspective and scaled orthographic camera models can be optimally calibrated including the intrinsic camera parameters. Moreover, the latter one is done without iteration if the \(L_2\) norm is used. It is also shown that the calibration can be inserted into a structure from motion algorithm. We also show that the scaled orthographic version can be powered by GPUs, yielding real-time performance.

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Titel: Weak-Perspective and Scaled-Orthographic Structure from Motion with Missing Data
verfasst von: Levente Hajder
Verlag: Springer International Publishing
Buch: Computer Vision, Imaging and Computer Graphics – Theory and Applications
Print ISBN: 978-3-030-12208-9

Electronic ISBN: 978-3-030-12209-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12209-6_7

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