Camera self-calibration: Theory and experiments

Faugeras, Olivier; Luong, Quang-Tuan; Maybank, Stephen J.

doi:10.1007/3-540-55426-2_37

Cited by 686 publications

(378 citation statements)

References 6 publications

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“…In contrast, methods based on the Kruppa equations [14,3,26] can not be recommended for general use, because they add a serious additional singularity to the already-restrictive ones intrinsic to the problem. If any 3D point projects to the same pixel and is viewed from the same distance in each image, a 'zoom' parameter can not be recovered from the Kruppa equations.…”

Section: Autocalibration For Non-planar Scenesmentioning

confidence: 99%

Autocalibration from planar scenes

Triggs

1998

Computer Vision — ECCV'98

258

169

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This paper describes a theory and a practical algorithm for the autocalibration of a moving projective camera, from ¡ £ ¢ ¥ ¤ v iews of a planar scene. The unknown camera calibration, and (up to scale) the unknown scene geometry and camera motion are recovered from the hypothesis that the camera's internal parameters remain constant during the motion. This work extends the various existing methods for non-planar autocalibration to a practically common situation in which it is not possible to bootstrap the calibration from an intermediate projective reconstruction. It also extends Hartley's method for the internal calibration of a rotating camera, to allow camera translation and to provide 3D as well as calibration information. The basic constraint is that the projections of orthogonal direction vectors (points at infinity) in the plane must be orthogonal in the calibrated camera frame of each image. Abstractly, since the two circular points of the 3D plane (representing its Euclidean structure) lie on the 3D absolute conic, their projections into each image must lie on the absolute conic's image (representing the camera calibration). The resulting numerical algorithm optimizes this constraint over all circular points and projective calibration parameters, using the inter-image homographies as a projective scene representation.

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Section: Autocalibration For Non-planar Scenesmentioning

confidence: 99%

Autocalibration from planar scenes

Triggs

1998

Computer Vision — ECCV'98

258

169

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“…The possibility of calibrating a camera based on the identification of matching points in several views of a scene taken by the same camera has been shown by Maybank and Faugeras ([13,4]). Using techniques of Projective Geometry they showed that each pair of views of the scene can be used to provide two quadratic equations in the five unknown parameters of the camera.…”

Section: Introductionmentioning

confidence: 99%

“…Using techniques of Projective Geometry they showed that each pair of views of the scene can be used to provide two quadratic equations in the five unknown parameters of the camera. A method of solving these equations to obtain the camera calibration has been reported in [13,4,12] based on directly solving these quadratic equations using continuation. It has been reported however that this method requires extreme accuracy of computation, and seems not to be suitable for routine use.…”

Section: Introductionmentioning

confidence: 99%

Euclidean reconstruction from uncalibrated views

Hartley¹

1994

Lecture Notes in Computer Science

215

183

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The possibility of calibrating a camera from image data alone, based on matched points identified in a series of images by a moving camera was suggested by Mayband and Faugeras. This result implies the possibility of Euclidean reconstruction from a series of images with a moving camera, or equivalently, Euclidean structure-from-motion from an uncalibrated camera. No tractable algorithm for implementing their methods for more than three images have been previously reported. This paper gives a practical algorithm for Euclidean reconstruction from several views with the same camera. The algorithm is demonstrated on synthetic and real data and is shown to behave very robustly in the presence of noise giving excellent calbration and reconstruction results.

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“…It tells us that given one point in one image, we can draw a line in the second image on which the corresponding point (i.e., the point representing the same physical point in space) necessarily lies. The epipolar geometry is captured by a r t s u r singular matrix called the fundamental matrix [13] …”

Section: Appendix: Epipolar Geometrymentioning

confidence: 99%

Interactive Common Illumination for Computer Augmented Reality

1997

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Abstract:The advent of computer augmented reality (CAR), in which computer generated objects mix with real video images, has resulted in many interesting new application domains. Providing common illumination between the real and synthetic objects can be very beneficial, since the additional visual cues (shadows, interreflections etc.) are critical to seamless real-synthetic world integration. Building on recent advances in computer graphics and computer vision, we present a new framework to resolving this problem. We address three specific aspects of the common illumination problem for CAR: (a) simplification of camera calibration and modeling of the real scene; (b) efficient update of illumination for moving CG objects and (c) efficient rendering of the merged world. A first working system is presented for a limited sub-problem: a static real scene and camera with moving CG objects. Novel advances in computer vision are used for camera calibration and user-friendly modeling of the real scene, a recent interactive radiosity update algorithm is adapted to provide fast illumination update and finally textured polygons are used for display. This approach allows interactive update rates on mid-range graphics workstations. Our new framework will hopefully lead to CAR systems with interactive common illumination without restrictions on the movement of real or synthetic objects, lights and cameras.

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Camera self-calibration: Theory and experiments

Cited by 686 publications

References 6 publications

Autocalibration from planar scenes

Autocalibration from planar scenes

Euclidean reconstruction from uncalibrated views

Interactive Common Illumination for Computer Augmented Reality

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