firstly, I wanted to know the metric unit of the 3d point we got from the opencv reprojectImageTo3D() function.
secondly, I have calibrated each camera individually with a chessboard with "mm" as metric unit and then use the opencv functions to calibrate the stereo system, rectify the stereo pair and then compute the disparity map.
Basically i want the distance of a center of a bounding box. so i compute the disparity map and reproject it to 3D with the reprojectImageTo3D() function and then i take from those 3D points, the one which correspond to the center of the bbox (x, y). But which image should i use to get the center of bbox? the rectified or the original?
Secondly, is it better to use the same camera model for a stereo system?
Thank you
During the calibration process (calibrateCamera) you have to give the points grid of your calibration target. The unit that you give there will then define the unit for the rest of the process.
When calling reprojectImageTo3D, you probably used the matrix Q output by stereoRectify, which takes in the individual calibrations (cameraMatrix1, cameraMatrix2). That's where the unit came from.
So in your case you get mm I guess.
reprojectImageTo3D has to use the rectified image, since the disparity is calculated using the rectified image (It wouldn't be properly aligned otherwise). Also, when calculating the disparity, it is calculated relative to the first image given (left one in the doc). So you should use the left rectified image if you computed the disparity like this: cv::StereoMatcher::compute(left, right)
I never had two different cameras, but it makes sense to use the same ones. I think that if you have very different color images, edges or any image difference, that could potentially influence the disparity quality. What is actually very important (unless you are only working with still pictures), is to use cameras that can be synchronized by hardware (e.g. GENLOCK signal: https://en.wikipedia.org/wiki/Genlock). If you have a bit of delay between left and right and a moving subject, the disparity can be wrong. This is also true for the calibration.
Hope this helps!