A practical comparison between Zhang's and Tsai's calibration approaches

Li, Wei; Gee, Trevor; Friedrich, Heide; Delmas, Patrice

doi:10.1145/2683405.2683443

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…The accuracy evaluation of camera calibration has a close relation with ours. Early attempts include the comparison of distortion models [31] and different calibration methods [32], [33]. The influences of measurement noise were also investigated as an important factor for calibration accuracy [34].…”

Section: Evaluation Of Calibration Accuracymentioning

confidence: 99%

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

et al. 2023

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This paper proposes an application to statistically predict the accuracy of single-image geometric camera calibration that uses given 2D-3D correspondences. Deriving both camera intrinsics and extrinsics from correspondences between a single image and a 3D shape, is important for the scene analysis when the optical system of the camera is lost, such as in the analyses of traffic accidents. It is unclear whether the single-image calibration will be successful in practice, particularly when the number of 2D-3D correspondences is small, even if we could assign accurate correspondences by manual labor. To this end, we perform a systematic evaluation of the camera parameter accuracy using synthetic environments. Based on the statistics observed during the experiments, our application predicts the calibration accuracy from simple variables (e.g., the area that correspondences could be given). Since the prediction process does not rely on 3D shapes, it provides an estimate of the success of the calibration before time-consuming processes, i.e., 3D scanning and 2D-3D correspondence mapping.INDEX TERMS Camera calibration, traffic accident reconstruction, computer vision.

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Section: Evaluation Of Calibration Accuracymentioning

confidence: 99%

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

et al. 2023

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“…(1) (2) are the focal lengths, are the coordinates of the principal point, are the lens distortion coefficients, and is the orientation and position of the camera. For close range photogrammetry, camera calibration requires the use of a calibration object with known geometry.…”

Section: Introductionmentioning

confidence: 99%

“…For close range photogrammetry, camera calibration requires the use of a calibration object with known geometry. The most common calibration methods are proposed by Tsai and Zhang [2]. Tsai's calibration relies on one observation of a 3D object while Zhang's calibration, considered as more accurate [2], is based on multiple observations of a flat surface with printed patterns.…”

Section: Introductionmentioning

confidence: 99%

Optimization Strategy of Camera Calibration for Close Range Photogrammetry

Lafon¹,

Vissiére²,

Bouazizi³

et al. 2022

Asian Society for Precision Engineering and Nanotechnology (ASPEN 2022)

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“…Zhang's algorithm requires only few images of a planar pattern at different orientations. Besides, this algorithm is computationally intensive and requires more time based on the quantity of the images [20].…”

Section: Introductionmentioning

confidence: 99%

“…Salvi used re‐projection error measurements in both distorted and undistorted images to evaluate the quality of the calibration algorithms [21]. Zhang's calibration procedure is more flexible, robust and accurate compared with Tsai calibration procedure, whereas Tsai algorithm takes less time than Zhang procedure [20]. Tsai's calibration approach [7] has been identified as the best alternative to Zhang among other procedures.…”

Section: Introductionmentioning

confidence: 99%

GA‐based camera calibration for vision‐assisted robotic assembly system

Pitchandi¹,

Subramanian²

2016

IET Computer Vision

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Vision sensors are employed in robotic assembly system to sense the dynamic environment and to position the manipulator precisely based on the sensor feedback. This process is termed as visual servoing. Precise calibration of the camera and camera/robot system are required to estimate the desired velocity of the robot and accurate positioning of the mating parts. In position‐based visual servoing, roughly calibrated camera leads to errors in robot/camera pose identification that affects the positional accuracy and time to reach the target position. A camera calibration procedure based on genetic algorithm (GA) is proposed in this study to estimate the intrinsic and extrinsic parameters of the camera model for improving positional accuracy and faster convergence. The proposed algorithm is implemented with two‐stage procedure and it comprises: determination of the camera parameters for distortion‐less model and reduction of re‐projection error through GA with linearly determined camera distortion‐less parameters as an initial solution. The proposed camera calibration algorithm has been tested and compared with the dataset images in the literature for its performance in terms of measurement accuracy. The result shows that the proposed algorithm has the capability to calibrate the distorted images with minimum re‐projection error using single image.

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A practical comparison between Zhang's and Tsai's calibration approaches

Cited by 19 publications

References 12 publications

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

Optimization Strategy of Camera Calibration for Close Range Photogrammetry

GA‐based camera calibration for vision‐assisted robotic assembly system

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