General Linear Cameras

Yu, Jingyi; McMillan, Leonard

doi:10.1007/978-3-540-24671-8_2

Cited by 112 publications

(115 citation statements)

References 13 publications

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“…Second, it sparked works on discrete camera models, where camera rays are calibrated individually from images of two or more planar calibration grids [85,189], an approach rediscovered by [191,477]. Third, the linear and quadratic versions of the two-plane model, when written in terms of back-projection matrices, are nothing else than particular instances of rational polynomial models; they are thus closely related to the division and rational models explained by Fitzgibbon [145] and Claus and Fitzgibbon [98] or the general linear cameras of Yu and McMillan [559], as well as others, which found a lot of interest recently. Martins et al may also be among the first to explicitly use lifted coordinates (cf.…”

Section: Camera Modelsmentioning

confidence: 97%

“…A second major difference is that this model allows to handle non-perspective distortions that may in principle be quite general. The linear two-plane model is actually identical to the general linear camera (GLC) model of Yu and McMillan [559], cf. Section 3.4.4, although it is formulated differently.…”

Section: First Version -"Linear Interpolation"mentioning

confidence: 99%

“…Several taxonomies and computational models have been proposed in the last years [390,405,445,472,559]. The main motivation of these works is to reason on the spatial distribution of camera rays; the mapping between rays and image points is of secondary importance although some of these works also propose such a mapping in order to obtain a full camera model.…”

Section: Models For the Distribution Of Camera Raysmentioning

confidence: 99%

“…Yu and McMillan proposed the so-called General Linear Camera model (GLC) [559,558], which parameterizes the set of camera rays by affine transformations of three basis rays. Concretely, they adopted a twoplane parameterization of rays (see also a paper by Gu et al [196] on this parameterization) as follows.…”

Section: General Linear Camera Model (Glc)mentioning

confidence: 99%

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Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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Section: Camera Modelsmentioning

confidence: 97%

Section: First Version -"Linear Interpolation"mentioning

confidence: 99%

Section: Models For the Distribution Of Camera Raysmentioning

confidence: 99%

Section: General Linear Camera Model (Glc)mentioning

confidence: 99%

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Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…Several non-pinhole camera models have also been proposed (e.g, [25,26]). However, none of them are powerful enough to handle disocclusions in practical scenes.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Image warping for compressing and spatially organizing a dense collection of images

Aliaga

Rosen

Popescu

et al. 2006

Signal Processing: Image Communication

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Image-based rendering (IBR) systems create photorealistic views of complex 3D environments by resampling large collections of images captured in the environment. The quality of the resampled images increases significantly with image capture density. Thus, a significant challenge in interactive IBR systems is to provide both fast image access along arbitrary viewpoint paths and efficient storage of large image data sets.We describe a spatial image hierarchy combined with an image compression scheme that meets the requirements of interactive IBR walkthroughs. By using image warping and exploiting image coherence over the image capture plane, we achieve compression performance similar to traditional motioncompensated schema, e.g., MPEG, yet allow image access along arbitrary paths. Furthermore, by exploiting graphics hardware for image resampling, we can achieve interactive rates during IBR walkthroughs.

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A Calibration Algorithm for POX-Slits Camera

Martins

Araújo

2005

Pattern Recognition and Image Analysis

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General Linear Cameras

Cited by 112 publications

References 13 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Image warping for compressing and spatially organizing a dense collection of images

A Calibration Algorithm for POX-Slits Camera

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