Optimization calibration of fish-eye camera and its research in 3D reconstruction

Chunsen Zhang; Lu Yan

doi:10.1117/12.2538198

14 February 2020 Optimization calibration of fish-eye camera and its research in 3D reconstruction

Chunsen Zhang, Lu Yan

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300U (2020) https://doi.org/10.1117/12.2538198
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

According to the characteristics of fish-eye camera, such as large field of view and super short focal length, the traditional camera calibration algorithm based on the small hole imaging model cannot achieve the calibration. This paper proposed a fish-eye camera calibration optimization based on the traditional Kannala model. Firstly, the camera imaging model and distortion type of the fish-eye camera are studied, and on the basis of the traditional Kannala model, the piecewise polynomial approximation model is established to realize the original model optimization. Then, the intrinsic parameters and distortion coefficients of the camera are obtained according to the traditional Kannala model and the optimization model，and the distortion correction images are obtained by intrinsic parameters and distortion coefficients. Finally, the advantages of this algorithm are quantitatively and qualitatively analyzed by using the re-projection error and the multiview stereo vision 3D reconstruction of the distorted correction image. The results indicate that the camera parameters and distortion coefficients were obtained by calibration to correct the original image and to carry out 3D reconstruction of multi-view stereo vision, and the reverse projection error analysis and 3D reconstruction visualization of the camera check are proved to be effective in the calibration of the optimized model camera.

Citation Download Citation

Chunsen Zhang and Lu Yan "Optimization calibration of fish-eye camera and its research in 3D reconstruction", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300U (14 February 2020); https://doi.org/10.1117/12.2538198

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KEYWORDS

Cameras

Distortion

3D modeling

Calibration

Image processing

Error analysis

Visual process modeling

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