You can buy known objects (expensive, for metrology calibration) or nowadays you could 3D print test objects. A simple one is to set up a flat board with white

You can buy known objects (expensive, for metrology calibration) or nowadays you could 3D print test objects. A simple one is to set up a flat board with white noise printed on it and then measure depth noise as a function of distance (eg fit a plane). The cameras usually aren't the problem. You just need two reasonably high quality machine vision cameras that are ideally hardware synced. There are geometric limits on how accurate you can be, related to the camera separation/baseline and how well you expect you can match at the sub pixel level. 0.1-0.25 px would be considered decent. Normally you'd design the problem in reverse eg what error do we need at the worst distance, field of view dictates lenses, etc. It can be very bespoke. Depth reconstruction is more reliant on matching algorithms and illumination. You can test stereo algorithms on benchmark datasets (The classics are Middlebury and KITTI). Illumination includes things like random dot projection or other artificial texture to aid matching + reconstruction.