I am doing a research in stereo vision and I am interested in accuracy of depth estimation in this question. It depends of several factors like:
If you wan't to know a bit more about accuracy of the approaches take a look at this site, although is no longer very active the results are pretty much state of the art. Take into account that a couple of the papers presented there went to create companies. What do you mean with real stereo vision system? If you mean commercial there aren't many, most of the commercial reconstruction systems work with structured light or directly scanners. This is because (you missed one important factor in your list), the texture is a key factor for accuracy (or even before that correctness); a white wall cannot be reconstructed by a stereo system unless texture or structured light is added. Nevertheless, in my own experience, systems that involve variational matching can be very accurate (subpixel accuracy in image space) which is generally not achieved by probabilistic approaches. One last remark, the distance between cameras is also important for accuracy: very close cameras will find a lot of correct matches and quickly but the accuracy will be low, more distant cameras will find less matches, will probably take longer but the results could be more accurate; there is an optimal conic region defined in many books. After all this blabla, I can tell you that using opencv one of the best things you can do is do an initial cameras calibration, use Brox's optical flow to find find matches and reconstruct.