Asking about Stereo Calibration

[nocardio]

Hi, thanks a lot for sharing the repository.

I would like to ask about the calibration process that you did. I performed my own-calibration program using python and opencv functions. I compare my calibration results with yours. It is not exactly similar, but the differences are not quite big. And I also notice that there is a sign-difference in the resulted transformation matrix.

This is my result. "K1": [[373.7949702163523, 0.0, 233.2121359920058], [0.0, 374.78872152463055, 326.7416010655098], [0.0, 0.0, 1.0]], "D1": [[-0.01524617660770033, 0.06377436171161646, 0.0059206313977930225, -0.0028066593724951575, 0.0]], "K2": [[372.2177084412977, 0.0, 233.72245228472624], [0.0, 372.74442474930663, 320.77904472579394], [0.0, 0.0, 1.0]], "D2": [[-0.029994968527599163, 0.08906159374301409, 0.0039004932620613697, -0.002926571337908015, 0.0]], "R": [[0.9998470404579204, 0.003139374608744723, -0.01720581339558163], [-0.0030159029330480568, 0.9999695503037905, 0.0071974158371902845], [0.017227884868318555, -0.007144423860674444, 0.9998260634683729]], "T": [[-0.10711754463310794], [0.000244465906715656], [-0.003588584164022485]],

I am wondering about the differences. I am aware that HL2 provides a rotated LF&RF frames in the opposite direction. In my case, I pre-process (clockwise-&counter-clockwise rotation) those images to have the same view first, and then process stereo-calibration. Did you also initially perform this pre-process step or just using the raw images captured from the sensors?

One more thing, I notice that the sign in your transformation matrix (RF-to-LF) is quite similar with the extrinsic parameters provided by the sensor. Did you perhaps perform some negation/inversion process to your stereo-calibration result? Or it was already in those condition?

Thank you very much and I'm sorry if these questions sound trivial :) -Nova

[cgsaxner]

Hi Nova!

I think your camera parameters look reasonable. The intrinsics as well as extrinsics of the cameras might be different for every HL2. The parameters I obtained and provide in this repo can be seen as a reference, and might even work for other HL2s, but there is no guarantee.

As for the larger differences, e.g. cx and cy being approximately swapped, and the sign difference in the extrinsic matrix, these can be explained by rotating the images. I calibrated both with the images "as are", and with rotated images, and obtained similar results as yours with the rotated images.

When using your calibration parameters, note that the plugin does NOT rotate the images. So you would have to modify the plugin to rotate the images or detected blobs accordingly, otherwise, the 3D reconstruction will not work!

Not a trivial question at all, the camera calibration is essential to for very accurate tracking, so this is actually a key step :)

[nocardio]

Hi Cgsaxner, thank you for your immediate reply 👍

Just as you mentioned, I modify the plugin to rotate the images and detect blobs accordingly. Using my existing calibrations, I can successfully calculate the triangulated points and match them to the points in the instrument markers. After I got the estimated pose of the marker, do you think I need to rotate/invert the matrix or just use it "as is" to your code?

Back to the calibration process, I also tried to follow the same calibration procedures as yours. I got the similar intrinsic parameters as yours (cx and cy are not being swapped). However, I got a really weird extrinsic. Do you perhaps know what I am missing here?

"K1": [[374.7974483204609, 0.0, 326.73365358676296], [0.0, 373.81077210981357, 245.79527427004624], [0.0, 0.0, 1.0]], "D1": [[-0.015265481008251279, 0.06359039857765747, 0.002794160375673042, 0.005908780510357354, 0.0]], "K2": [[372.7694238525443, 0.0, 318.18290019438365], [0.0, 372.2345752817822, 233.61868821911386], [0.0, 0.0, 1.0]], "D2": [[-0.030133088719034466, 0.08979537726539062, -0.0030097401391500096, -0.003940485405324927, 0.0]], "R": [[0.4292777049358266, 0.38645072182454976, 0.8163188664035115], [-0.29300277251440143, -0.7953622972622787, 0.530611148952361], [0.8543243104020297, -0.4669632273476736, -0.22820016862645276]], "T": [[-0.359926220903595], [-0.47292623355191216], [0.9347921061650112]],

Once again, thank you very much ^^

[cgsaxner]

I think the last thing you need to consider is the rigToWorld / camToWorld transform, which is needed to transform the detected 3D points from camera reference frame to world reference frame. Since you effectively change the camera reference frame by rotating the images, you would also need to flip this transform by 90° around the z-Axis. However, I'm not 100 % sure, I would recommend to have some offline evaluation code using recordings or known points and just try it out. This additional level of complexity was the reason why I abandoned rotating the images in the first place :)

As for the calibration problem, indeed, the extrinsic matrix looks strange. Not sure what is going wrong there without seeing the data. Are you using the same images as for the flipped calibration, just without rotating them?

[nocardio]

Hi, sorry for the late reply. For the weird extrinsic result (flipped calibration), I used the "as-is" images, without additional rotation, with left-image and right-image are originally counter-clockwise and clockwise rotated by 90degrees, respectively.

[ghost]

Hello, I had a doubt regarding how to compute the transformation of right camera to left camera.

//Code from instrument tracker.h where we have to place the calibration information

    // definition of the model points
    cv::Mat pointsModel = (cv::Mat_<float>(5, 3) <<
        2.57336e-7f, 2.42391e-6f, 3.36463e-6f,
        0.0442368f, 0.0349529f, 0.0726091f,
        -0.0936104f, 0.0113667f, 0.0732692f,
        0.000176568f, -0.000130322f, 0.108399f,
        -0.00748328f, 0.0856192f, 0.0642646f);

    // definition of camera parameters
    float lfFxHL2 = 362.76819f;
    float lfFyHL2 = 363.63627f;
    float lfCxHL2 = 316.65636f;
    float lfCyHL2 = 231.20529f;
    cv::Mat lfDistHL2 = (cv::Mat_<float>(1, 5) <<
        -0.01818f, 0.01685f, -0.00494f, 0.00170f, 0.0f);

    float rfFxHL2 = 364.68835f;
    float rfFyHL2 = 364.63003f;
    float rfCxHL2 = 321.11678f;
    float rfCyHL2 = 233.32648f;
    cv::Mat rfDistHL2 = (cv::Mat_<float>(1, 5) <<
        -0.02002f, 0.01935f, -0.00306f, -0.00216f, 0.0f);
    // ---------How to find this matrix? Rf2Lf ------------
    cv::Mat rfToLf = (cv::Mat_<float>(4, 4) <<
        -0.999988716987f, 0.004749329171f, 0.000098853715f, -0.001400949679f,
        -0.004750308829f, -0.999856389093f, -0.016267629061f, -0.098276565847f,
        0.000021579193f, -0.016267915098f, 0.999867668481f, 0.002508806869f,
        0.0f, 0.0f, 0.0f, 1.0f);

Using openCV camera calibration, I was able to find the intrinsic parameters & distortion coefficient for both the cameras.

Thanks in advance!