During the last decade pose measurement technologies have gained an increasing interest in the computer vision. The vision-based pose measurement method has been widely applied in complex environments. However, the pose measurement error is a problem in the measurement applications. It grows rapidly with increasing measurement range. In order to meet the demand of high accuracy in large measurement range, a measurement error reduction solution to the vision-based pose measurement method, called Global Control Point Calibration (GCPC), is proposed. GCPC is an optimized process of existing visual pose measurement methods. The core of GCPC is to divide the measurement error into two types: the control point error and the control space error. Then by creating the global control points as well as performing error calibration of object pose, the two errors are processed. The control point error can be eliminated and the control space error is minimized. GCPC is experimented on the moving target in the camera's field of view. The results show that the RMS error is 0.175° in yaw angle, 0.189° in pitch angle, and 0.159° in roll angle, which demonstrate that GCPC works effectively and stably.