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Estimating pose parameters of a 3D rigid object based on a 2D monocular image is a fundamental problem in computer vision. State-of-the-art methods usually assume that certain feature correspondences are available a priori between the input image and object's 3D model. This presumption makes the problem more algebraically tractable. However, when there is no feature correspondence available a priori, how to estimate the pose of a truly 3D object using just one 2D monocular image is still not well solved. In this article, a new contour-based method which solves both the pose estimation problem and the feature correspondence problem simultaneously and iteratively is proposed. The outer contour of the object is firstly extracted from the input 2D grey-level image, then a tentative point correspondence relationship is established between the extracted contour and object's 3D model, based on which object's pose parameters will be estimated; the newly estimated pose parameters are then used to revise the tentative point correspondence relationship, and the process is iterated until convergence. Experiment results are promising, showing that the authors' method has fast convergence speed and good convergence radius.