Pose estimation by monocular is finding the pose of the object by a single image of feature points on the object, which must meet the requirements of detecting all the feature points and matching them in the image. But it will be difficult to obtain the correct pose if part of the feature points are occluded when the object moving a large scale. We proposed a method for finding the pose on the condition that the correspondences between the object points and the image points are unknown. The method combines two algorithms: one algorithm is SoftAssign, which constructs a weight matrix of feature points and image points, and determines the correspondences by iteration loop processing; the other algorithm is OI(orthogonal iteration), which derives an iterative algorithm which directly computes orthogonal and globally convergent rotation matrices.We nest the two algorithms into one iteration loop.An appropriate pose will be chosen from a set of reference poses as the initial pose of object at the beginning of the loop, then we process the weight matrix to confirm the correspondences and calculate the optimal solution of rotation matrices alternately until the object space collinearity error is less than the threshold, each estimation will be closer to the truth pose than the last one through every iteration loop. Experimentally, the method proved to be efficient and have a high precision pose estimation of 3D object with large-scale motion.