An algorithm for self calibration from several views

Hartley,

doi:10.1109/cvpr.1994.323923

Cited by 102 publications

(47 citation statements)

References 8 publications

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“…This enables less complex algebraic problem formulations, allows to work with fewer images, and often gives more accurate results. As for perspective cameras [207], the most used motion is pure rotation about the optical center; an approximate rotation is sufficient if the scene is sufficiently far away. In the following, we describe several approaches, first ones based on special types of motion (but with unknown motion parameters), then ones using knowledge of the motion parameters.…”

Section: Special or Known Motionsmentioning

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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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: Special or Known Motionsmentioning

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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“…In the second category are methods which do not use a calibration object and are generally referred to as selfcalibration [12,16]. In this approach a camera is moved in a static scene.…”

Section: Introductionmentioning

confidence: 99%

Camera calibration using spheres: a semi-definite programming approach

Agrawal

Davis

2003

Proceedings Ninth IEEE International Conference on Computer Vision

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“…The intrinsic parameters describe the camera's imaging geometric characteristics, and the extrinsic parameters represent the camera's orientation and position with respect to the world coordinate system. Many approaches to camera calibration have been proposed and they can be classified into two categories: using calibration objects [6,13,16,11,1,2,8,12,14,15], and self-calibration [7,5,9]. As we know, the occluding contour of a sphere is projected to a conic in the perspective image [1,2,5,12,14].…”

Section: Introductionmentioning

confidence: 99%

Interpreting Sphere Images Using the Double-Contact Theorem

Ying

Zha

2006

Computer Vision – ACCV 2006

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Abstract. An occluding contour of a sphere is projected to a conic in the perspective image, and such a conic is called a sphere image. Recently, it has been discovered that each sphere image is tangent to the image of the absolute conic at two double-contact image points. The double-contact theorem describes the properties of three conics which all have double contact with another conic. This theorem provides a linear algorithm to find the another conic if these three conics are given. In this paper, the double-contact theorem is employed to interpret the properties among three sphere images and the image of the absolute conic. The image of the absolute conic can be determined from three sphere images using the double-contact theorem. Therefore, a linear calibration method using three sphere images is obtained. Only three sphere images are required, and all five intrinsic parameters are recovered linearly without making assumptions, such as, zero-skew or unitary aspect ratio. Extensive experiments on simulated and real data were performed and shown that our calibration method is an order of magnitude faster than previous optimized methods and a little faster than former linear methods while maintaining comparable accuracy.

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An algorithm for self calibration from several views

Cited by 102 publications

References 8 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera calibration using spheres: a semi-definite programming approach

Interpreting Sphere Images Using the Double-Contact Theorem

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