To solve the problem that the reconstructed 3D model of a single image has high uncertainty, a network model based on depth image estimation, spherical projection mapping and 3D generative adversarial network was proposed. Firstly, the depth image of the input image was obtained by the depth estimator, which was helpful for the further analysis of the image. Secondly, the obtained depth image was converted into a 3D model by spherical projection mapping. Finally, 3D generative adversarial network was utilized to judge the authenticity of the reconstructed 3D model, so as to obtain 3D model closer to reality. In the comparison experiments with LVP algorithm which learning view priors for 3D reconstruction, the proposed model has the Intersection-over-Union (IoU) increased by 20.1% and the Charmfer Distance (CD) decreased by 13.2%. Theoretical analysis and simulation results show that the proposed model has good generalization ability in the 3D model reconstruction of a single image.