Camera Matrix Calibration Using Circular Control Points and Separate Correction of the Geometric Distortion Field

Rudakova, Victoria; Monasse, Pascal

doi:10.1109/crv.2014.34

Cited by 11 publications

(6 citation statements)

References 13 publications

(18 reference statements)

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“…The polynomial model requires a little higher degrees than the rational model to achieve the precision on the order of 1/100 pixels. This high degree raises no (24). In fact, the results of all the tested parametric models are visually identical, according to the accuracy shown in Tables IV and V. computational problem for the polynomial model, which can be solved linearly after a correct conditioning.…”

Section: Discussionmentioning

confidence: 53%

“…The polynomial and the rational models are not adapted to global bundle adjustment, where the internal/external parameters and the distortion model are estimated simultaneously. The distortion correction must be dealt with as an independent and previous step to camera calibration [24]. All the models are able to correct the distortion at sub-pixel precision better than 0.1 pixel and the polynomial and the rational models seem to perform better when the distortion is large.…”

Section: Discussionmentioning

confidence: 99%

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A Precision Analysis of Camera Distortion Models

Tang

Gioi

Monasse

et al. 2017

IEEE Trans. on Image Process.

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This paper addresses the question of identifying the right camera direct or inverse distortion model, permitting a high subpixel precision to fit to real camera distortion. Five classic camera distortion models are reviewed and their precision is compared for direct or inverse distortion. By definition, the three radially symmetric models can only model a distortion radially symmetric around some distortion center. They can be extended to deal with non-radially symmetric distortions by adding tangential distortion components, but still may be too simple for very accurate modeling of real cameras. The polynomial and the rational models instead miss a physical or optical interpretation, but can cope equally with radially and non-radially symmetric distortions. Indeed, they do not require the evaluation of a distortion center. When requiring high precisions, we found that the distortion modeling must also be evaluated primarily as a numerical problem. Indeed, all models except the polynomial involve a non-linear minimization, which increases the numerical risk. The estimation of a polynomial distortion model leads instead to a linear problem, which is secure and much faster. We concluded by extensive numerical experiments that, although high degree polynomials were required to reach a high precision of 1/100 pixels, such polynomials were easily estimated and produced a precise distortion modeling without overfitting. Our conclusion is validated by three independent experimental setups: the models were compared first on the lens distortion database of the Lensfun library by their distortion simulation and inversion power; second by fitting real camera distortions estimated by a non parametric algorithm; and finally by the absolute correction measurement provided by the photographs of tightly stretched strings, warranting a high straightness.

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Section: Discussionmentioning

confidence: 53%

Section: Discussionmentioning

confidence: 99%

A Precision Analysis of Camera Distortion Models

Tang

Gioi

Monasse

et al. 2017

IEEE Trans. on Image Process.

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“…Circular markers are often claimed to provide a higher accuracy (up to 1/50 of a pixel (Heikkila, 2000)) but the larger they appear in the image the more important are corrections due to perspective and camera distortion (Rudakova and Monasse, 2014). The high effort needed here and the low flexibility in resolution and object distance makes them difficult to use.…”

Section: State Of the Artmentioning

confidence: 99%

Automatic Camera System Calibration With a Chessboard Enabling Full Image Coverage

Wohlfeil

Grießbach

Ernst

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Geometric camera calibration is a mandatory prerequisite for many applications in computer vision and photogrammetry. Especially when requiring an accurate camera model the effort for calibration can increase dramatically. For the calibration of the stereo-camera used for optical navigation a new chessboard based approach is presented. It is derived from different parts of existing approaches which, taken separately, are not able to meet the requirements. Moreover, the approach adds one novel main feature: It is able to detect all visible chessboard fields with the help of one or more fiducial markers simply sticked on a chessboard (AprilTags). This allows a robust detection of one or more chessboards in a scene, even from extreme perspectives. Except for the acquisition of the calibration images the presented approach enables a fully automatic calibration. Together with the parameters of the interior and relative orientation the full covariance matrix of all model parameters is calculated and provided, allowing a consistent error propagation in the whole processing chain of the imaging system. Even though the main use case for the approach is a stereo camera system it can be used for a multi-camera system with any number of cameras mounted on a rigid frame.</p>

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“…Improving calibration and reconstruction accuracy continue to be active areas of research [23,32]. Novel calibration algorithms [30,33], and replacing bulky and expensive 3D calibration objects with 1D wands have contributed to reducing manual calibration effort [28]. However, little work has focused on improving the procedure or user experience of the calibration.…”

Section: Calibration Tasksmentioning

confidence: 99%

Wavi

Franjcic

Woźniak

Kasparavičiūtė

et al. 2016

Proceedings of the 18th International Conference on Human-Computer Interaction With Mobile Devices and Services

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Motion tracking systems are gaining popularity and have a number of applications in research, entertainment, and arts. These systems must be calibrated before use. This process requires extensive user effort to determine a 3D coordinate system with acceptable accuracy. Usually, this is achieved by rapidly manipulating a calibration device (e.g. a calibration wand) in a volume for a set amount of time. While this is a complex spatial input task, improving the user experience of calibration inspired little research. This paper presents the design, implementation, and evaluation of WAVI -a prototype device mounted on a calibration wand to jointly provide visual and tactile feedback during the calibration process. We conducted a user study that showed that the device significantly increases calibration quality without increasing user effort. Based on our experiences with WAVI, we present new insights for improving motion tracking calibration and complex spatial input.

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Camera Matrix Calibration Using Circular Control Points and Separate Correction of the Geometric Distortion Field

Cited by 11 publications

References 13 publications

A Precision Analysis of Camera Distortion Models

A Precision Analysis of Camera Distortion Models

Automatic Camera System Calibration With a Chessboard Enabling Full Image Coverage

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