In monocular vision pose estimation, the topological relationship between objective feature points and image feature points where there are multiple feature points is difficult to determine. An algorithm based on multiple feature points topological determination was proposed to solve this problem where the correspondences are unknown. By mounting multiple feature points on the object, enough proper feature points for pose computing were guaranteed when the object is large-scale moving, which can improve the precision of pose estimation. The algorithm nested the iteration process of topological determination and the iteration process of pose computing into one iteration loop, solving them simultaneously. The pose estimation iteration process was based on para-perspective projection model, where the coordination of the projection of the object gravity center used as the initial parameter of iteration is not needed. The iteration process of topological determination was transformed into a solution of assignment problem. Each topological determination can obtain a better pose estimation in every pose estimation iteration loop. The results of multiple poses experiment and precision comparison experiment prove that the algorithm is qualified for the high precision pose estimation of 3D object with large scale motion, with the root mean square error 0.272° in the range of -120°-120°.