Estimating parameters of noncentral catadioptric systems using bundle adjustment

Gonçalves, Nuno; Araújo, Hélder

doi:10.1016/j.cviu.2008.06.004

Cited by 15 publications

(15 citation statements)

References 7 publications

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“…Overall, a line of sight (or rather, a half-line), together with these non-geometric properties, make what Grossberg and Nayar termed a raxel, a sort of tiny camera associated with each individual pixel of the imaging system. Such a ray-based imaging model has been used recently by several researchers to devise calibration and structure-from-motion algorithms, for example by Ramalingam et al [414,415,418,477], Dunne et al [123,124,125], and Gonçalves and Araújo [180]. Ray-based calibration approaches are described in Section 5.1.2.…”

Section: Discrete Camera Models 73mentioning

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: Discrete Camera Models 73mentioning

confidence: 99%

“…Gonçalves and Araújo used this approach to first compute pixel-ray matches, to which they then fitted a catadioptric model [180].…”

Section: Planar Gridsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…Computing all of those unknown parameters at one time is difficult due to the highly nonlinear nature of the problem. In [4], a two-step calibration algorithm is proposed by using the black box model [3] first, followed by an iterative bundle adjustment. In particular, assuming the known mirror and the camera's intrinsic parameters, a simplified model concerning only the relative position between the mirror and the camera can be computed by a closed-form solution using the imaged mirror boundary [5].…”

mentioning

confidence: 99%

“…Lacking a compact parameterized description, the model suffers low accuracy and flexibility. The complete model [4] considers the system as a combination of a reflective mirror and a projective camera, and models them separately with 20 intrinsic parameters. Computing all of those unknown parameters at one time is difficult due to the highly nonlinear nature of the problem.…”

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confidence: 99%

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Noncentral catadioptric camera calibration using a generalized unified model

2013

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This Letter proposes a generalized unified model (GUM) for the calibration of noncentral catadioptric cameras. Releasing the constraint on the projection center and the orientation of the imaging plane that the traditional unified projection model has, the new model is able to well compensate the misalignment between the mirror and the camera. Being a compact and approximate central model, the GUM inherits the flexibility and simplicity from the unified model while maintaining accuracy even under severe misalignment. The calibration algorithm to compute the describing parameters of the model is also given. With the GUM, the calibration of central or noncentral systems could be treated with equal simplicity (or complexity). Experiments on both synthetic data and real images proved our success.Omnidirectional catadioptric cameras featuring the advantage of large field-of-view are being increasingly used in lots of fields such as surveillance and robotics. Calibration is a prerequisite step in most of the applications involving 3D geometries. Depending on whether they pose a single viewpoint, catadioptric cameras can be classified as central or noncentral imaging systems. For central systems, there exist some proper calibration models, among which the unified projection model is the most popular one [1]. Based on this model, a calibration method using planar patterns is proposed by Mei and Rives [2]. The central system has the advantages that the mature computing theories for the perspective cameras are still applicable. However, in practice, most of the catadioptric cameras are noncentral considering the misalignment between the reflective mirror and the camera, let alone the mirror types that do not belong to the limited "central list" such as spherical mirrors.For noncentral systems, great efforts have been made to set up an accurate projection model and compute the describing parameters. Currently there are two main models: the black box model [3] and the complete model [4]. In the black box model, the system is considered a black box and the calibration task is converted into setting up a list of correspondence between the imaging pixels and their corresponding 3D rays. Special structured light patterns [3] could be used to finish this task. Lacking a compact parameterized description, the model suffers low accuracy and flexibility. The complete model [4] considers the system as a combination of a reflective mirror and a projective camera, and models them separately with 20 intrinsic parameters. Computing all of those unknown parameters at one time is difficult due to the highly nonlinear nature of the problem. In [4], a two-step calibration algorithm is proposed by using the black box model [3] first, followed by an iterative bundle adjustment. In particular, assuming the known mirror and the camera's intrinsic parameters, a simplified model concerning only the relative position between the mirror and the camera can be computed by a closed-form solution using the imaged mirror boundary [5]. The largest drawbac...

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Image rectification for prism-based stereoscopic optical systems

Kuznetsov

Gorevoy

2019

Computer Vision and Image Understanding

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Estimating parameters of noncentral catadioptric systems using bundle adjustment

Cited by 15 publications

References 7 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Noncentral catadioptric camera calibration using a generalized unified model

Image rectification for prism-based stereoscopic optical systems

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