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In this paper, we propose a robust algorithm for 3D object pose estimation from a single 2D image. The proposed pose estimation algorithm is based on modifying the traditional image projection error function to a sum of squared image projection errors weighted by their associated distances. By using an Euler angle representation, we formulate the energy minimization for the pose estimation problem as searching a global minimum solution. Based on this framework, the proposed algorithm employs robust techniques to detect outliers in a coarse-to-fine fashion, thus providing very robust pose estimation. Our experiments show that the algorithm outperforms previous methods under noisy conditions.