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A rational polynomial camera (RPC) model is a kind of generic sensor model that can be used in different remote sensing systems to model the relationship between object space and image space and transform image data to conform to a map projection. Unlike traditional physical camera models, an RPC model has many coefficients (a total of 80), and these coefficients do not have a physical interpretation. This represents a difficult challenge for the mapping community. For RPC refinement, many solutions, including direct and indirect methods, have been developed. One of them, the recent developed generic method has been shown to be a robust method. Because the generic method can simulate the camera's exterior parameters, it can be used in any geometric situation. Even so, the performance of bundle adjustment with the generic method is still unknown. In this paper, through experiments with a stereo pair and a stereo triplet, the capability of high-accuracy geopositioning based on the generic method is demonstrated. We first give a brief review of previous bundle adjustment methods based on RPC. Then, the bundle adjustment algorithm based on the generic method is introduced in detail. We finally present the experiments with both IKONOS and QuickBird imageries. The experiments show that the bundle adjustment based on the generic method can reach subpixel accuracy in image space and submeter accuracy in object space.