Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning

introduction

Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks).

Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video.

There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self-calibration method, and camera calibration method based on active vision.

In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014).

Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010).

OpenCV

Deep Learning

https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx-QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg-zy1CXgeEwRHbfcCHeA=w1280

Engineering of Camera Calibration

Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms.

The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process.

I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps.

* The chessboard is used as a pattern of alternating black and white squares,

- which ensures that there is no bias toward one side or the other in measurement.

* The image must be an grayscale (single-channel) image.

- img - Input image. It should be grayscale and float32 type.

* gradianet x and y direction together (for better detection)

- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required

* using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities.

- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified

> img - Input image. It should be grayscale and float32 type.

> blockSize - It is the size of neighborhood considered for corner detection

> ksize - Aperture parameter of the Sobel derivative used.

> k - Harris detector free parameter in the equation.

* contours to remove some noise:

- cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids

* subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates

cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners

- image Input single-channel, 8-bit or float image.

- corners Initial coordinates of the input corners and refined coordinates provided for output.

- winSize Half of the side length of the search window. (5*5 will be 11)

- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix.

- criteria Criteria for termination of the iterative process of corner refinement.

* remove duplicate corners: for example corners are in less than 5 pixels should be remove

Reference:

https://theailearner.com/tag/cv2-cornersubpix/

https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html

#Camera_Calibration #Camera-resectioning

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Source code

Basic camear calibration source code by using OpenCV library in Jupyter notebook

https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb

Reference

Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras)

Book Chapter (Springer):

Camera Calibration and Video Stabilization Framework for Robot Localization https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12

IEEE paper:

Pattern image significance for camera calibration https://ieeexplore.ieee.org/abstract/document/8305440

Camera calibration for multi-modal robot vision based on image quality assessment https://www.researchgate.net/profile/Farshid-Pirahansiah/publication/288174690_Camera_calibration_for_multi-modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera-calibration-for-multi-modal-robot-vision-based-on-image-quality-assessment.pdf

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