I am trying to calculate the stereo baseline (possibly in cm) between two arbitrary frames, after a 6DoF tracking has being performed. I would like to extract further additional information by comparing two frames but I need somehow to figure out what is the distance between the camera position in frame 1 and, say frame 10. Any suggestion on how to proceed?
I have done this in a more explicit and non-automated way with a 3D tracking software that solves the camera, then I physically measure the distance of the solved camera on frame 1 and 10 but I'm stuck conceptually on how to do this in ArCore
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I use single-camera calibration with checkerboard and I used one fix position of the camera to do the calibration. Now my question is if I use the same position but change the height of the camera then do I need to do calibration again? If no then will I get the same result by using the different height of the camera?
In my case, I changed the height of the camera but the position of the camera was the same. And I got a different result when I changed height. So I was wondering that may I need to do again calibration of the camera or not?
please help me out.
Generally speaking, and to achieve greatest accuracy, you will need to recalibrate the camera whenever it is moved. However, if the lens mount is rigid enough w.r.t the sensor, you may get away with only updating the extrinsic calibration, especially if your accuracy requirements are modest.
To see why this is the case notice that, unless you have a laboratory-grade rig holding and moving the camera, you can't just change the height only. With a standard tripod, for example, there will in general be a motion in all three axes amounting to a significant fraction of the sensor's size, which will be reflected in visible motion of several pixel with respect to the scene.
Things get worse / more complicated when you also add rotation to re-orient the field of view, since a mechanical mount will not, in general, rotate the camera around its optical center (i.e. the exit pupil of the lens), and therefore every rotation necessarily comes with an additional translation.
In your specific case, since you are only interested in measurements on a plane, and therefore can compute everything using homographies, refining the extrinsic calibration amounts to just recomputing the world-to-image scale. This can easily be achieved by taking one or more images of objects of known size on the plane - a calibration checkerboard is just such an object.
I'm working with an stereo camera setup that has auto-focus (which I cannot turn off) and a really low baseline of less than 1cm.
Auto-focus process can actually change any intrinsic parameter of both cameras (as focal length and principal point, for example) and without a fix relation (left camera may add focus while right one decrease it). Luckily cameras always report the current state of intrinsics with great precision.
On every frame an object of interest is being detected and disparities between camera images are calculated. As baseline is quite low and resolution is not the greatest, performing stereo triangulation leads to quite poor results and for this matter several succeeding computer vision algorithms relay only on image keypoints and disparities.
Now, disparities being calculated between stereo frames cannot be directly related. If principal point changes disparities will be in very different magnitudes after the auto-focus process.
Is there any way to relate keypoint corners and/or disparities between frames after auto-focus process? For example, calculate where would the object lie in the image with the previous intrinsics?
Maybe using a bearing vector towards object and then seek for intersection with image plane defined by previous intrinsics?
Quite challenging your project, perhaps these patents could help you in some way:
Stereo yaw correction using autofocus feedback
Autofocus for stereo images
Depth information for auto focus using two pictures and two-dimensional Gaussian scale space theory
I have a set of non-coplanar points with unknown 3D position (I am not limited with points number, let's say 8-10 of them), and at least 3 different views (number of views also not limited) of these points in 2D images. I have also estimation for rotation and scale for every point set on pictures that corresponds to real points, also an estimation of the euclidean distance between every two camera positions that images were taken at.
Is this data enough to find camera pose after taking another additional picture with these points (to find as precisely as possible)? If not, what are minimal additional data need to have to achieve this?
UPDATE: In this specific case I needed the function recoverPose() from calib3d module
Yes, this is possible. Depending on the algorithms (and the availability of some pre-calibration), you can obtain the relative positions of two cameras using a minimum of 5 to 8 points.
Beware that the point correspondences must be available, i.e. the points must be known in pairs.
A computer-tomography device has a roentgen matrix of 20x500 dots with the resolution of 2mm in each direction. This matrix is rotating around a belt, shich transports items to be analysed. A special reconstruction algorithm produces 3D model of the items from many-many matixes captured from all 360 perspectives ( one image per 1° angle).
The problem is, the reconstruction algorithm is very sensitive to the belt speed/position. Measuring the belt position requires quite complicated and expensive positining sensors and very fine mechanics.
I wonder if it is possible to calculate the belt velocity using the roentgen-image itself. It has a width of 40mm and should be sufficient for capturing the movement. The problem is, the movement is always in 2 directions - rotation and X (belt). For those working in CT-area, are you aware of some applications/publishings about such direct measurement of the belt/table velocity?
P.S.: It is not vor medical application.
Hmm, interesting idea.
Are you doing a full 180 degree for the reconstruction? I'd go with the 0 and 180 degree cone beam images. They should be approximately the same, minus some non-linear stuff, artifacts, Poisson noise and difference in 'shadows' and scattering due to perspective.
You could translate the 180 image along the negative x-axis, to the direction opposite of the movement. You could then subtract images at some suitable intervals along this axis. When the absolute of the sum hits a minimum the translation should be approx at the distance the object has moved between 0 and 180, as the mirror images cancel each other out partially.
This could obviously be ruined by artifacts and wonkily shaped heavy objects. Still, worth a try. I'm assuming your voltage is pretty high if you are doing industrial stuff.
EDIT: "A special reconstruction algorithm produces 3D model of the items from many-many matixes captured from all 360 perspectives ( one image per 1° angle)."
So yes, you are using 180+ degrees. You could then perhaps use multiple opposite images for a more robust routine. How do you get the full circle? Are you shooting through the belt?
I have to find the camera position using several images from a football field. Using these images I can find the homography that transforms the points in the 3D model (in meters) into the points in the image (pixels). Each picture can have a different focal lengths, hence the camera intrinsics can change. I'm using OpenCV in C++.
I've found solutions for similar problems but either you already have the camera calibrated, with a fixed focal length (in this case you only need one image) or you don't have the camera calibrated but you consider that its intrinsic doesn't change (in this case you need at least 3 images).
Is there anyway to find the camera position in the 3D model without knowing anything besides the homographies (with different focal lengths)?
Short answer: No.
If you don't have any additional information about the cameras, there is no way to uniquely position the cameras. There would be multiple combinations of camera attributes & positions that would produce the same image.
You may be able to constrain the camera position along a certain 3D line, and reduce the problem to 1 degree of freedom, if that helps.