Aerial optical cameras are the primary method for capturing high-resolution images to produce large-scale mapping products. To improve aerial photography efficiency, multiple cameras are often used in combination to generate large-format virtual central projection images. This paper presents a high-precision method for directly transforming raw images obtained from a dual-camera system mounted at an oblique angle into virtual central projection images, thereby enabling the construction of low-cost, large-format aerial camera systems. The method commences with an adaptive sub-block in the overlapping regions of the raw images to extract evenly distributed feature points, followed by iterative relative orientation to improve accuracy and reliability. A global projection transformation matrix is constructed, and the sigmoid function is employed as a weighted distance function for image stitching. The results demonstrate that the proposed method produces more evenly distributed feature points, higher relative orientation accuracy, and greater reliability. Simulation analysis of image overlap indicates that when the overlap exceeds 7%, stitching accuracy can be better than 1.25 μm. The aerial triangulation results demonstrate that the virtual central projection images satisfy the criteria for the production of 1:1000 scale mapping products.