In monocular vision-guided high-precision inter-platform pose measurement, existing methods require an accurate 3D model of the target platform and are unable to eliminate the impact of 3D model errors on pose measurement. To address this issue, iterative optimization is performed on the 3D model of the target platform and pose, and a new monocular vision measurement method is proposed. Specifically, the target platform’s 3D model is modeled using a set of sparse 3D keypoints. By leveraging multi-view geometric constraint information in sequential images, the sparse 3D keypoint set of the target and 6D pose are treated as parameters to be solved. An objective function is established to minimize object-space residuals, and through solving this optimization problem, iterative optimization of the sparse 3D keypoint set and pose is achieved. Additionally, a sliding window combined with a keyframe selection strategy is adopted to realize real-time and online high-precision monocular vision measurement. Experimental results demonstrate that, through iterative optimization of the sparse 3D keypoint set and pose, the proposed method achieves real-time, online high-precision monocular pose. measurement under the condition of an inaccurate 3D model of target platform, while simultaneously improving the accuracy of the target’s 3D model.