Single Image Camera Calibration with Lenticular Arrays for Augmented Reality

Schillebeeckx, Ian; Pless, Robert

doi:10.1109/cvpr.2016.358

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Similarly, circles [25], vanishing points [7], [26], and low-rank textures [27] are known to be useful assumptions for single-image calibration. In the augmented reality application, fiducial markers are designed for estimating intrinsic parameters [28]. To avoid the lack of generalization, we will investigate a general method for geometric calibration, which does not rely on planar scenes or any other special assumptions.…”

Section: B Single-image Full Calibrationmentioning

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: B Single-image Full Calibrationmentioning

confidence: 99%

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

et al. 2023

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“…The search for corresponding epipolar lines was limited to lines with similar barcodes. Schillebeeckx and Pless 9 introduced a calibration object based on a flat lenticular array that creates a color-coded light field in which the observed color changes depending on the angle from which it is viewed. Other studies have also addressed environments where it is difficult to calibrate cameras, such as medical endoscopes.…”

Section: Related Workmentioning

confidence: 99%

Calibration of multiple sparsely distributed cameras using a mobile camera

Shishido

Kitahara

2019

Proceedings of the Institution of Mechanical Engineers, Part P:

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In sports science research, there are many topics that utilize the body motion of athletes extracted by motion capture system, since motion information is valuable data for improving an athlete’s skills. However, one of the unsolved challenges in motion capture is extraction of athletes’ motion information during the actual game or match, as placing markers on athletes is a challenge during game play. In this research, the authors propose a method for acquisition of motion information without attaching a marker, utilizing computer vision technology. In the proposed method, the three-dimensional world joint position of the athlete’s body can be acquired using just two cameras without any visual markers. Furthermore, the athlete’s three-dimensional joint position during game play can also be obtained without complicated preparations. Camera calibration that estimates the projective relationship between three-dimensional world and two-dimensional image spaces is one of the principal processes for the respective three-dimensional image processing, such as three-dimensional reconstruction and three-dimensional tracking. A strong-calibration method, which needs to set up landmarks with known three-dimensional positions, is a common technique. However, as the target space expands, landmark placement becomes increasingly complicated. Although a weak-calibration method does not need known landmarks, the estimation precision depends on the accuracy of the correspondence between image captures. When multiple cameras are arranged sparsely, sufficient detection of corresponding points is difficult. In this research, the authors propose a calibration method that bridges multiple sparsely distributed cameras using mobile camera images. Appropriate spacing was confirmed between the images through comparative experiments evaluating camera calibration accuracy by changing the number of bridging images. Furthermore, the proposed method was applied to multiple capturing experiments in a large-scale space to verify its robustness. As a relevant example, the proposed method was applied to the three-dimensional skeleton estimation of badminton players. Subsequently, a quantitative evaluation was conducted on camera calibration for the three-dimensional skeleton. The reprojection error of each part of the skeletons and standard deviations were approximately 2.72 and 0.81 mm, respectively, confirming that the proposed method was highly accurate when applied to camera calibration. Consequently, a quantitative evaluation was conducted on the proposed calibration method and a calibration method using the coordinates of eight manual points. In conclusion, the proposed method stabilizes calibration accuracy in the vertical direction of the world coordinate system.

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“…Chen et al proposed a refractive calibration method for an underwater stereo camera system where both cameras are looking through multiple parallel flat refractive interfaces. In research on improving calibration accuracy, the estimation error was minimized by calculating the epipolar geometry from dynamic silhouettes [6] and color codes [7]. The motion barcode of Gil et al is a binary temporal sequence for lines that indicate the existence of at least one foreground pixel on that line.…”

Section: Related Workmentioning

confidence: 99%

Calibration method for sparse multi-view cameras by bridging with a mobile camera

Shishido

Kitahara

2017

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.

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Single Image Camera Calibration with Lenticular Arrays for Augmented Reality

Cited by 6 publications

References 21 publications

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

Calibration of multiple sparsely distributed cameras using a mobile camera

Calibration method for sparse multi-view cameras by bridging with a mobile camera

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