Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Batteoui, I. El; Saaidi, Abderrahim; Satori, Khalid

doi:10.1007/s13319-015-0065-4

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…The minimization of the cost function makes it possible to estimate the camera parameters. Similarly, the method [25] is based on the projection of two points of the 3D scene in the planes of two images only to self-calibrate a camera with variable parameters. Similarly, [26] the author selected three corresponding points having the largest value of ZNCC so that they are the projection of three points of an unknown 3D scene.…”

Section: A Traditional Approaches To Camera Self-calibrationmentioning

confidence: 99%

Convolutional Neural Networks for Camera Self-Calibration With Varying Intrinsic Parameters

2023

Journal of Southwest Jiaotong University

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Stereo camera self-calibration is a complex problem that has been tackled by computer vision and pattern recognition communities, and significant progress has been made. In the same context, this work proposes a new and robust fully automated approach of camera self-calibration with varying intrinsic parameters from two images of an unknown 3D object. This method is based on deep learning, such as convolutional neural networks (CNN), to reduce mathematical complexities and estimation time to a minimum. These networks use a special architecture that is particularly well adapted to estimate the intrinsic parameters of the camera through back propagation and gradient descent. First, our learningbased method introduces the self-calibration equations formulated into a non-linear cost function. The new optimization of this cost function makes it possible to estimate the intrinsic parameters of the cameras. The strong point of the proposed architecture is that it merges the high effectiveness of the CNN algorithm with the four pillars of our non-linear cost function formulation (two images, four matches, any camera and unknown 3D scene) to form the fast and robust proposed network. The experimental results on synthetic and real data prove the performance of the proposed technique in terms of simplicity, precision, and convergence.

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Section: A Traditional Approaches To Camera Self-calibrationmentioning

confidence: 99%

Convolutional Neural Networks for Camera Self-Calibration With Varying Intrinsic Parameters

2023

Journal of Southwest Jiaotong University

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“…Wu and Wang [12] first achieve the simplified Kruppa equation by decomposing the fundamental matrix and then gain the intrinsic parameters by the constraints of two sets of equations. In contrast of the above two methods, some works [13] utilize the constraint relationship of the known coded mark points in the scene to complete the optimization of the intrinsic parameters. In [13], Batteoui et al use parallelograms in at least five scenes to identify the constraint equations of the intrinsic camera parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast of the above two methods, some works [13] utilize the constraint relationship of the known coded mark points in the scene to complete the optimization of the intrinsic parameters. In [13], Batteoui et al use parallelograms in at least five scenes to identify the constraint equations of the intrinsic camera parameters. Then, the real-time varying intrinsic parameters are obtained through optimization.…”

Section: Introductionmentioning

confidence: 99%

A Real‐Time Deformation Monitoring Method for Large‐Scale Structure Based on Relay Camera

Liu

2021

Journal of Sensors

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In the process of the deformation monitoring for large-scale structure, the mobile vision method is often used. However, most of the existent researches rarely consider the real-time property and the variation of the intrinsic parameters. This paper proposes a real-time deformation monitoring method for the large-scale structure based on a relay camera. First, we achieve the real-time pose-position relationship by using the relay camera and the coded mark points whose coordinates are known. The real-time extrinsic parameters of the measuring camera are then solved according to the constraint relationship between the relay camera and the measuring camera. Second, the real-time intrinsic parameters of the measuring camera are calculated based on the real-time constraint relationship among the extrinsic parameters, the intrinsic parameters, and the fundamental matrix. Finally, the coordinates of the noncoded measured mark points, which are affixed to the surface of the structure, are achieved. Experimental results show that the accuracy of the proposed method is higher than 1.8 mm. Besides, the proposed method also possesses the real-time and automation property.

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Inserting and tracking a plane object in a three-dimensional scene

Oufqir¹,

Abderrahmani²,

Satori³

2021

Multimed Tools Appl

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Accurate Self-calibration of Camera with Variable Intrinsic Parameters from Unknown 3D Scene

Cited by 4 publications

References 26 publications

Convolutional Neural Networks for Camera Self-Calibration With Varying Intrinsic Parameters

Convolutional Neural Networks for Camera Self-Calibration With Varying Intrinsic Parameters

A Real‐Time Deformation Monitoring Method for Large‐Scale Structure Based on Relay Camera

Inserting and tracking a plane object in a three-dimensional scene

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