To achieve high-precision and fine 3D reconstruction with the Gaofen-7 satellite sub-meter-level images， this paper proposes a method that focuses on the relative error correction of stereo pairs， image horizontal plane correction， and semi-global matching optimization， forming a fine 3D reconstruction pipeline. First， concerning the relative error in the orientation model of Gaofen-7 stereo images， the geometric constraint relationship of the connection point among images is used to eliminate the systematic error of the rational function model. Second， a horizontal correction method based on the projection plane of the object is used to correct the original image； this eliminates the large inclination error difference between stereo images and provides a better data basis for subsequent processes. In the dense matching stage， global publicly available digital elevation model （DEM） data are used as the disparity constraint. AD-Census， which consider both the grayscale and feature information of the image， is employed as the matching cost metric， addressing the matching error problem caused by repeated texture and improving the digital surface model （DSM） production. The results of experiments conducted using Gaofen-7stereo images covering areas in Ningxia and Xinjiang indicate that the proposed method can improve the relative error accuracy from 0.847 and 0.725 pixels to 0.652 and 0.593 pixels， respectively， representing up to 23.02% improvement. The horizontal correction method based on the projection plane of the object can significantly eliminate the geometric distortion caused by the difference in large inclination angle， and good-quality DSM products can be obtained， especially for the repetitive texture of small-scale dense building areas.