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2012 | OriginalPaper | Buchkapitel

2. Passive 3D Imaging

verfasst von : Stephen Se, Nick Pears

Erschienen in: 3D Imaging, Analysis and Applications

Verlag: Springer London

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Abstract

We describe passive, multiple-view 3D imaging systems that recover 3D information from scenes that are illuminated only with ambient lighting. Much of the material is concerned with using the geometry of stereo 3D imaging to formulate estimation problems. Firstly, we present an overview of the common techniques used to recover 3D information from camera images. Secondly, we discuss camera modeling and camera calibration as an essential introduction to the geometry of the imaging process and the estimation of geometric parameters. Thirdly, we focus on 3D recovery from multiple views, which can be obtained using multiple cameras at the same time (stereo), or a single moving camera at different times (structure from motion). Epipolar geometry and finding image correspondences associated with the same 3D scene point are two key aspects for such systems, since epipolar geometry establishes the relationship between two camera views, while depth information can be inferred from the correspondences. The details of both stereo and structure from motion, the two essential forms of multiple-view 3D reconstruction technique, are presented. Towards the end of the chapter, we present several real-world applications.

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Vorheriges Kapitel Introduction

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You may wish to compare l ^T x=0 to two well-known parameterizations of a line in the (x,y) plane, namely: ax+by+c=0 and y=mx+c and, in each case, write down homogeneous coordinates for the point x and the line l.

We use a tilde to differentiate n-tuple inhomogeneous coordinates from (n+1)-tuple homogeneous coordinates.

We need to use a variety of image coordinate normalizations in this chapter. For simplicity, we will use the same subscript n, but it will be clear about how the normalization is achieved.

Skew models a lack of orthogonality between the two image sensor sampling directions. For most imaging situations it is zero.

The same homogeneous image coordinates up to scale or the same inhomogeneous image coordinates.

Due to the scale equivalence of homogeneous coordinates.

No three points collinear.

‘Approximately’, because of noise in the imaged corner positions supplied to the calibration process.

Extrinsic parameters are always not known in a structure from motion problem, they are part of what we are trying to solve for. Intrinsic parameters may or may not be known, depending on the application.

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Titel: Passive 3D Imaging
verfasst von: Stephen Se
Nick Pears
Verlag: Springer London
Buch: 3D Imaging, Analysis and Applications
Print ISBN: 978-1-4471-4062-7

Electronic ISBN: 978-1-4471-4063-4

Copyright-Jahr: 2012
DOI: https://doi.org/10.1007/978-1-4471-4063-4_2

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